Building Energy Consumption Prediction: An Extreme Deep Learning Approach

Towards sustainable architecture: Enhancing green building energy consumption prediction with integrated variational autoencoders and self-attentive gated recurrent units from multifaceted datasets

Global awareness of sustainable development has heightened interest in green buildings as a key strategy for reducing energy consumption and carbon emissions. Accurate prediction of energy consumption plays a vital role in developing effective energy management and conservation strategies. This study addresses these challenges by proposing an advanced deep learning framework that integrates Time-Dependent Variational Autoencoder (TD-VAE) with Adaptive Gated Self-Attention GRU (AGSA-GRU). The framework incorporates self-attention mechanisms and Multi-Task Learning (MTL) strategies to capture long-term dependencies and complex patterns in energy consumption time series data, while simultaneously optimizing prediction accuracy and anomaly detection. Experiments on two public green building energy consumption datasets validate the effectiveness of our proposed approach. Our method achieves a prediction accuracy of 93.2%, significantly outperforming traditional deep learning methods and existing techniques. ROC curve analysis demonstrates our model's robustness, achieving an Area Under the Curve (AUC) of 0.91 while maintaining a low false positive rate (FPR) and high true positive rate (TPR). This study presents an efficient solution for green building energy consumption prediction, contributing significantly to energy conservation, emission reduction, and sustainable development in the construction industry.

Sentiment Classification Method Based on Blending of Emoticons and Short Texts

With the development of Internet technology, short texts have gradually become the main medium for people to obtain information and communicate. Short text reduces the threshold of information production and reading by virtue of its short length, which is in line with the trend of fragmented reading in the context of the current fast-paced life. In addition, short texts contain emojis to make the communication immersive. However, short-text content means it contains relatively little information, which is not conducive to the analysis of sentiment characteristics. Therefore, this paper proposes a sentiment classification method based on the blending of emoticons and short-text content. Emoticons and short-text content are transformed into vectors, and the corresponding word vector and emoticon vector are connected into a sentencing matrix in turn. The sentence matrix is input into a convolution neural network classification model for classification. The results indicate that, compared with existing methods, the proposed method improves the accuracy of analysis.

CBLSTM-AE: A Hybrid Deep Learning Framework for Predicting Energy Consumption

Multisource energy data, including from distributed energy resources and its multivariate nature, necessitate the integration of robust data predictive frameworks to minimise prediction error. This work presents a hybrid deep learning framework to accurately predict the energy consumption of different building types, both commercial and domestic, spanning different countries, including Canada and the UK. Specifically, we propose architectures comprising convolutional neural network (CNN), an autoencoder (AE) with bidirectional long short-term memory (LSTM), and bidirectional LSTM BLSTM). The CNN layer extracts important features from the dataset and the AE-BLSTM and LSTM layers are used for prediction. We use the individual household electric power consumption dataset from the University of California, Irvine to compare the skillfulness of the proposed framework to the state-of-the-art frameworks. Results show performance improvement in computation time of 56% and 75.2%, and mean squared error (MSE) of 80% and 98.7% in comparison with a CNN BLSTM-based framework (EECP-CBL) and vanilla LSTM, respectively. In addition, we use various datasets from Canada and the UK to further validate the generalisation ability of the proposed framework to underfitting and overfitting, which was tested on real consumers’ smart boxes. The results show that the framework generalises well to varying data and constraints, giving an average MSE of ∼0.09 across all datasets, demonstrating its robustness to different building types, locations, weather, and load distributions.

Cloud-edge collaboration based transferring prediction of building energy consumption

Building energy consumption (BEC) prediction often requires constructing a corresponding model for each building based historical data. However, the constructed model for one building is difficult to be reused in other buildings. Recent approaches have shown that cloud-edge collaboration architecture is promising in realizing model reuse. How to complete the reuse of cloud energy consumption prediction models at the edge and reduce the computational cost of the model training is one of the key issues that need to be solved. To handle the above problems, a cloud-edge collaboration based transferring prediction method for BEC is proposed in this paper. Specifically, a model library stored prediction models for different types of buildings is constructed based the historical energy consumption data and the long short-term memory (LSTM) network in the cloud firstly; then, the similarity measurement strategies of time series with different granularity are given, and the model to be transferred from the model library is matched by analyzing the similarity between observation data uploaded to the cloud and the historical data collected in the cloud; finally, the fine-tuning strategy of the matching prediction model is given, and this model is fine-tuned at the edge to achieve its reuse in concrete application scenarios. Experiments on practical datasets reveal that compared with the prediction model which doesn’t utilize the transfer strategy, the proposed prediction model has better performance according to MAE and RMSE. Experimental results also confirm that the proposed method effectively reduces the computational cost of the network training at the edge.

Influencing Factors Evaluation of Machine Learning-Based Energy Consumption Prediction

Modern computing resources, including machine learning-based techniques, are used to maintain stability between the demand and supply of electricity. Machine learning is widely used for the prediction of energy consumption. The researchers present several artificial intelligence and machine learning-based methods to improve the prediction accuracy of energy consumption. However, the discrepancy between actual energy consumption and predicted energy consumption is still challenging. Various factors, including changes in weather, holidays, and weekends, affect prediction accuracy. This article analyses the overall prediction using error curve learning and a hybrid model. Actual energy consumption data of Jeju island, South Korea, has been used for experimental purposes. We have used a hybrid ML model consisting of Catboost, Xgboost, and Multi-layer perceptron for the prediction. Then we analyze the factors that affect the week-ahead (WA) and 48 h prediction results. Mean error on weekdays is recorded as 2.78%, for weekends 2.79%, and for special days it is recorded as 4.28%. We took into consideration significant predicting errors and looked into the reasons behind those errors. Furthermore, we analyzed whether factors, such as a sudden change in temperature and typhoons, had an effect on energy consumption. Finally, the authors have considered the other factors, such as public holidays and weekends, to analyze the significant errors in the prediction. This study can be helpful for policymakers to make policies according to the error-causing factors.

A Comprehensive Review on Residential Demand Side Management Strategies in Smart Grid Environment

The ever increasing demand for electricity and the rapid increase in the number of automatic electrical appliances have posed a critical energy management challenge for both utilities and consumers. Substantial work has been reported on the Home Energy Management System (HEMS) but to the best of our knowledge, there is no single review highlighting all recent and past developments on Demand Side Management (DSM) and HEMS altogether. The purpose of each study is to raise user comfort, load scheduling, energy minimization, or economic dispatch problem. Researchers have proposed different soft computing and optimization techniques to address the challenge, but still it seems to be a pressing issue. This paper presents a comprehensive review of research on DSM strategies to identify the challenging perspectives for future study. We have described DSM strategies, their deployment and communication technologies. The application of soft computing techniques such as Fuzzy Logic (FL), Artificial Neural Network (ANN), and Evolutionary Computation (EC) is discussed to deal with energy consumption minimization and scheduling problems. Different optimization-based DSM approaches are also reviewed. We have also reviewed the practical aspects of DSM implementation for smart energy management.

Predictive Maintenance in Building Facilities: A Machine Learning-Based Approach

The operation and maintenance of buildings has seen several advances in recent years. Multiple information and communication technology (ICT) solutions have been introduced to better manage building maintenance. However, maintenance practices in buildings remain less efficient and lead to significant energy waste. In this paper, a predictive maintenance framework based on machine learning techniques is proposed. This framework aims to provide guidelines to implement predictive maintenance for building installations. The framework is organised into five steps: data collection, data processing, model development, fault notification and model improvement. A sport facility was selected as a case study in this work to demonstrate the framework. Data were collected from different heating ventilation and air conditioning (HVAC) installations using Internet of Things (IoT) devices and a building automation system (BAS). Then, a deep learning model was used to predict failures. The case study showed the potential of this framework to predict failures. However, multiple obstacles and barriers were observed related to data availability and feedback collection. The overall results of this paper can help to provide guidelines for scientists and practitioners to implement predictive maintenance approaches in buildings.

A Review of Deep Learning Techniques for Forecasting Energy Use in Buildings

Buildings account for a significant portion of our overall energy usage and associated greenhouse gas emissions. With the increasing concerns regarding climate change, there are growing needs for energy reduction and increasing our energy efficiency. Forecasting energy use plays a fundamental role in building energy planning, management and optimization. The most common approaches for building energy forecasting include physics and data-driven models. Among the data-driven models, deep learning techniques have begun to emerge in recent years due to their: improved abilities in handling large amounts of data, feature extraction characteristics, and improved abilities in modelling nonlinear phenomena. This paper provides an extensive review of deep learning-based techniques applied to forecasting the energy use in buildings to explore its effectiveness and application potential. First, we present a summary of published literature reviews followed by an overview of deep learning-based definitions and techniques. Next, we present a breakdown of current trends identified in published research along with a discussion of how deep learning-based models have been applied for feature extraction and forecasting. Finally, the review concludes with current challenges faced and some potential future research directions.

Towards Efficient Building Designing: Heating and Cooling Load Prediction via Multi-Output Model

In the current technological era, energy-efficient buildings have a significant research body due to increasing concerns about energy consumption and its environmental impact. Designing an appropriate energy-efficient building depends on its layout, such as relative compactness, overall area, height, orientation, and distribution of the glazing area. These factors directly influence the cooling load (CL) and heating load (HL) of residential buildings. An accurate prediction of these load facilitates a better management of energy consumption and enhances the living standards of inhabitants. Most of the traditional machine learning (ML)-based approaches are designed for single-output (SO) prediction, which is a tedious task due to separate training processes for each output with low performance. In addition, these approaches have a high level of nonlinearity between input and output, which need more enhancement in terms of robustness, predictability, and generalization. To tackle these issues, we propose a novel framework based on gated recurrent unit (GRU) that reliably predicts the CL and HL concurrently. To the best of our knowledge, we are the first to propose a multi-output (MO) sequential learning model followed by utility preprocessing under the umbrella of a unified framework. A comprehensive set of ablation studies on ML and deep learning (DL) techniques is done over an energy efficiency dataset, where the proposed model reveals an incredible performance as compared to other existing models.

Time Series Forecasting with Multi-Headed Attention-Based Deep Learning for Residential Energy Consumption

Predicting residential energy consumption is tantamount to forecasting a multivariate time series. A specific window for several sensor signals can induce various features extracted to forecast the energy consumption by using a prediction model. However, it is still a challenging task because of irregular patterns inside including hidden correlations between power attributes. In order to extract the complicated irregular energy patterns and selectively learn the spatiotemporal features to reduce the translational variance between energy attributes, we propose a deep learning model based on the multi-headed attention with the convolutional recurrent neural network. It exploits the attention scores calculated with softmax and dot product operation in the network to model the transient and impulsive nature of energy demand. Experiments with the dataset of University of California, Irvine (UCI) household electric power consumption consisting of a total 2,075,259 time-series show that the proposed model reduces the prediction error by 31.01% compared to the state-of-the-art deep learning model. Especially, the multi-headed attention improves the prediction performance even more by up to 27.91% than the single-attention.

A Building Energy Consumption Prediction Method Based on Integration of a Deep Neural Network and Transfer Reinforcement Learning

With respect to the problem of the low accuracy of traditional building energy prediction methods, this paper proposes a novel prediction method for building energy consumption, which is based on the seamless integration of the deep neural network and transfer reinforcement learning (DNN-TRL). The method introduces a stack denoising autoencoder to extract the deep features of the building energy consumption, and shares the hidden layer structure to transfer the common information between different building energy consumption problems. The output of the DNN model is used as the input of the Sarsa algorithm to improve the prediction performance of the target building energy consumption. To verify the performance of the DNN-TRL algorithm, based on the data recorded by American Power Balti Gas and Electric Power Company, and compared with Sarsa, ADE-BPNN, and BP-Adaboost algorithms, the experimental results show that the DNN-TRL algorithm can effectively improve the prediction accuracy of the building energy consumption.

Prediction of the Energy Consumption of School Buildings

The energy consumption of a constructed facility is a primary concern as a result of its impact on the total energy expenditure. It has been found that up to 70% of the power consumption in Saudi Arabia are caused by building structures and air conditioning (AC). Energy consumption in government-constructed buildings constitutes a considerable ≈13% of the consumption of the total energy in Saudi Arabia. Therefore, the government of Saudi Arabia initiated the Saudi Energy Efficiency Program (SEEP) that goals to lower the domestic energy severity by roughly 30% by 2030. This paper introduces a study carried out in Eastern Province in Saudi Arabia to identify factors influencing the consumption of energy in school facilities (which are built of concrete in hot and humid climate zones), investigate the correlation between those factors and their impacts on the consumption of energy in school facilities, and finally, develop a prediction model for the energy consumption of school facilities. The study was based on the utilization of 352 real-world datasets of energy consumption of operating schools across Eastern Province in Saudi Arabia. The developed energy prediction model considers eleven identified factors that influence the consumption of energy of constructed schools. The identified factors were utilized as input variables to build the model. A systematic search among different neural network (NN) design architectures was conducted to identify the optimal network model. Validation of the developed model on eight real-world cases demonstrated that the accuracy of the developed model was about 87.5%. Moreover, the findings of this study indicate that the weakest correlation between the input variables was recorded as −0.015 between “type of school” and “AC capacity,” while the strongest correlation was recorded as 0.95 between the variables of “number of classrooms” and “total air-conditioned area (sqm),” followed by “total air-conditioned area (sqm)” and “number of students,” which was recorded as 0.90. It is worth noting that “AC capacity” was the most significant predictor, which increased exponentially for high values of energy consumption, followed by “total school roof area.” The study also found that the age of the schools had a very small impact on energy consumption, although the age of the schools varied from 11 to 51 years. This was probably due to a good maintenance system applied by the Ministry of Education. The implication of the developed prediction model was that the model can be used by the Ministry of Education to predict the energy consumption and its associated cost for public school buildings for the purpose of budget allocation. The model may be utilized as a stand-alone application, or it can be integrated with an existing building information module (BIM)-based system.

Energy Efficiency and Thermal Performance of Office Buildings Integrated with Passive Strategies in Coastal Regions of Humid and Hot Tropical Climates in Madagascar

Researchers have used passive strategies, such as the implementation of thermal insulation and the use of phase change materials (PCM), in several studies, but some problems have not yet been solved. It is the case of showing the real effect of external shading combined with thermal insulation and phase change materials to improve the thermal performance and energy efficiency of office buildings in tropical coastal areas. Another pending problem to be solved is to define the impact produced by passive strategies on the performance of workers in office buildings in coastal zones. It is with a view to answering all these questions that this study was envisaged with the main objective of evaluating, analyzing, comparing, and discussing the effect of thermal insulation and phase change materials on thermal comfort and energy demand in coastal areas of hot and humid tropical climates located in the island of Madagascar. In this sense, hourly climate data for the past 30 years have served as the basis for assessing environmental conditions of future climate. It was found that the PCMs have a more significant effect on the coastal zone of hot climates than humid tropical climates. The results of the statistical analyses showed that the application of passive strategies stabilizes indoor air temperatures to between 23 °C and 28 °C in the offices, which is the recommended comfort range in these regions. In the coastal regions of Madagascar, up to 30% of cooling energy is expected to be reduced by combining the introduction of thermal insulation and PCM materials.

Towards Efficient Electricity Forecasting in Residential and Commercial Buildings: A Novel Hybrid CNN with a LSTM-AE based Framework

Due to industrialization and the rising demand for energy, global energy consumption has been rapidly increasing. Recent studies show that the biggest portion of energy is consumed in residential buildings, i.e., in European Union countries up to 40% of the total energy is consumed by households. Most residential buildings and industrial zones are equipped with smart sensors such as metering electric sensors, that are inadequately utilized for better energy management. In this paper, we develop a hybrid convolutional neural network (CNN) with an long short-term memory autoencoder (LSTM-AE) model for future energy prediction in residential and commercial buildings. The central focus of this research work is to utilize the smart meters’ data for energy forecasting in order to enable appropriate energy management in buildings. We performed extensive research using several deep learning-based forecasting models and proposed an optimal hybrid CNN with the LSTM-AE model. To the best of our knowledge, we are the first to incorporate the aforementioned models under the umbrella of a unified framework with some utility preprocessing. Initially, the CNN model extracts features from the input data, which are then fed to the LSTM-encoder to generate encoded sequences. The encoded sequences are decoded by another following LSTM-decoder to advance it to the final dense layer for energy prediction. The experimental results using different evaluation metrics show that the proposed hybrid model works well. Also, it records the smallest value for mean square error (MSE), mean absolute error (MAE), root mean square error (RMSE) and mean absolute percentage error (MAPE) when compared to other state-of-the-art forecasting methods over the UCI residential building dataset. Furthermore, we conducted experiments on Korean commercial building data and the results indicate that our proposed hybrid model is a worthy contribution to energy forecasting.

Solving the Cold-Start Problem in Short-Term Load Forecasting Using Tree-Based Methods

An energy-management system requires accurate prediction of the electric load for optimal energy management. However, if the amount of electric load data is insufficient, it is challenging to perform an accurate prediction. To address this issue, we propose a novel electric load forecasting scheme using the electric load data of diverse buildings. We first divide the electric energy consumption data into training and test sets. Then, we construct multivariate random forest (MRF)-based forecasting models according to each building except the target building in the training set and a random forest (RF)-based forecasting model using the limited electric load data of the target building in the test set. In the test set, we compare the electric load of the target building with that of other buildings to select the MRF model that is the most similar to the target building. Then, we predict the electric load of the target building using its input variables via the selected MRF model. We combine the MRF and RF models by considering the different electric load patterns on weekdays and holidays. Experimental results demonstrate that combining the two models can achieve satisfactory prediction performance even if the electric data of only one day are available for the target building.

A Learning-Based Framework for Error Compensation in 3D Printing

As a typical cyber-physical system, 3D printing has developed very fast in recent years. There is a strong demand for mass customization, such as printing dental crowns. However, the accuracy of the 3D printed objects is low compared with traditional methods. The main reason is that the model to be printed is arbitrary and usually the quantity is small. The deformation is affected by the shape of the object and there is a lack of a universal method for the error compensation. It is neither easy nor economical to perform the compensation manually. In this paper, we present a framework for the automatic error compensation. We obtain the shape by technologies such as 3D scanning. And we use the "3D deep learning" method to train a deep neural network. For a specific task, such as dental crown printing, the network can learn the function of deformation when a large amount of data is used for training. To the best of our knowledge, this is the first application of the deep neural network to the error compensation in 3D printing. And we propose the "inverse function network" to compensate for the error. We use four types of deformations of the dental crowns to verify the performance of the neural network: 1) translation; 2) scaling up; 3) scaling down; and 4) rotation. The convolutional AutoEncoder structure is employed for the end-to-end learning. The experiments show that the network can predict and compensate for the error well. By introducing the new method, we can improve the accuracy with little need for increasing the hardware cost.

Evaluation of Photovoltaic Power Generation by using Deep Learning in Solar Panels Installed in Buildings

Southern Taiwan has excellent solar energy resources that remain largely unused. This study incorporated a measure that aids in providing simple and effective power generation efficiency assessments of solar panel brands in the planning stage of installing these panels on roofs. The proposed methodology can be applied to evaluate photovoltaic (PV) power generation panels installed on building rooftops in Southern Taiwan. In the first phase, this study selected panels of the BP3 series, including BP350, BP365, BP380, and BP3125, to assess their PV output efficiency. BP Solar is a manufacturer and installer of photovoltaic solar cells. This study first derived ideal PV power generation and then determined the suitable tilt angle for the PV panels leading to direct sunlight that could be acquired to increase power output by panels installed on building rooftops. The potential annual power outputs for these solar panels were calculated. Climate data of 2016 were used to estimate the annual solar power output of the BP3 series per unit area. The results indicated that BP380 was the most efficient model for power generation (183.5 KWh/m2-y), followed by BP3125 (182.2 KWh/m2-y); by contrast, BP350 was the least efficient (164.2 KWh/m2-y). In the second phase, to simulate meteorological uncertainty during hourly PV power generation, a surface solar radiation prediction model was developed. This study used a deep learning–based deep neural network (DNN) for predicting hourly irradiation. The simulation results of the DNN were compared with those of a backpropagation neural network (BPN) and a linear regression (LR) model. In the final phase, the panel of module BP3125 was used as an example and demonstrated the hourly PV power output prediction at different lead times on a solar panel. The results demonstrated that the proposed method is useful for evaluating the power generation efficiency of the solar panels.

Forecasting Energy Use in Buildings Using Artificial Neural Networks: A Review

During the past century, energy consumption and associated greenhouse gas emissions have increased drastically due to a wide variety of factors including both technological and population-based. Therefore, increasing our energy efficiency is of great importance in order to achieve overall sustainability. Forecasting the building energy consumption is important for a wide variety of applications including planning, management, optimization, and conservation. Data-driven models for energy forecasting have grown significantly within the past few decades due to their increased performance, robustness and ease of deployment. Amongst the many different types of models, artificial neural networks rank among the most popular data-driven approaches applied to date. This paper offers a review of the studies published since the year 2000 which have applied artificial neural networks for forecasting building energy use and demand, with a particular focus on reviewing the applications, data, forecasting models, and performance metrics used in model evaluations. Based on this review, existing research gaps are identified and presented. Finally, future research directions in the area of artificial neural networks for building energy forecasting are highlighted.

Energy Management through Cost Forecasting for Residential Buildings in New Zealand

Over the last two decades, the residential building sector has been one of the largest energy consumption sectors in New Zealand. The relationship between that sector and household energy consumption should be carefully studied in order to optimize the energy consumption structure and satisfy energy demands. Researchers have made efforts in this field; however, few have concentrated on the association between household energy use and the cost of residential buildings. This study examined the correlation between household energy use and residential building cost. Analysis of the data indicates that they are significantly correlated. Hence, this study proposes time series methods, including the exponential smoothing method and the autoregressive integrated moving average (ARIMA) model for forecasting residential building costs of five categories of residential buildings (one-storey house, two-storey house, townhouse, residential apartment and retirement village building) in New Zealand. Moreover, the artificial neutral networks (ANNs) model was used to forecast the future usage of three types of household energy (electricity, gas and petrol) using the residential building costs. The t-test was used to validate the effectiveness of the obtained ANN models. The results indicate that the ANN models can generate acceptable forecasts. The primary contributions of this paper are twofold: (1) Identify the close correlation between household energy use and residential building costs; (2) provide a new clue for optimizing energy management.

District Heating Load Prediction Algorithm Based on Feature Fusion LSTM Model

The smart district heating system (SDHS) is an important element of the construction of smart cities in Northern China; it plays a significant role in meeting heating requirements and green energy saving in winter. Various Internet of Things (IoT) sensors and wireless transmission technologies are applied to monitor data in real-time and to form a historical database. The accurate prediction of heating loads based on massive historical datasets is the necessary condition and key basis for formulating an optimal heating control strategy in the SDHS, which contributes to the reduction in the consumption of energy and the improvement in the energy dispatching efficiency and accuracy. In order to achieve the high prediction accuracy of SDHS and to improve the representation ability of multi-time-scale features, a novel short-term heating load prediction algorithm based on a feature fusion long short-term memory (LSTM) model (FFLSTM) is proposed. Three characteristics, namely proximity, periodicity, and trend, are found after analyzing the heating load data from the aspect of the hourly time dimension. In order to comprehensively utilize the data’s intrinsic characteristics, three LSTM models are employed to make separate predictions, and, then, the prediction results based on internal features and other external features at the corresponding moments are imported into the high-level LSTM model for fusion processing, which brings a more accurate prediction result of the heating load. Detailed comparisons between the proposed FFLSTM algorithm and the-state-of-art algorithms are conducted in this paper. The experimental results show that the proposed FFLSTM algorithm outperforms others and can obtain a higher prediction accuracy. Furthermore, the impact of selecting different parameters of the FFLSTM model is also studied thoroughly.

A Framework for Prediction of Household Energy Consumption Using Feed Forward Back Propagation Neural Network

Energy is considered the most costly and scarce resource, and demand for it is increasing daily. Globally, a significant amount of energy is consumed in residential buildings, i.e., 30–40% of total energy consumption. An active energy prediction system is highly desirable for efficient energy production and utilization. In this paper, we have proposed a methodology to predict short-term energy consumption in a residential building. The proposed methodology consisted of four different layers, namely data acquisition, preprocessing, prediction, and performance evaluation. For experimental analysis, real data collected from 4 multi-storied buildings situated in Seoul, South Korea, has been used. The collected data is provided as input to the data acquisition layer. In the pre-processing layer afterwards, several data cleaning and preprocessing schemes are applied to the input data for the removal of abnormalities. Preprocessing further consisted of two processes, namely the computation of statistical moments (mean, variance, skewness, and kurtosis) and data normalization. In the prediction layer, the feed forward back propagation neural network has been used on normalized data and data with statistical moments. In the performance evaluation layer, the mean absolute error (MAE), mean absolute percentage error (MAPE), and root mean squared error (RMSE) have been used to measure the performance of the proposed approach. The average values for data with statistical moments of MAE, MAPE, and RMSE are 4.3266, 11.9617, and 5.4625 respectively. These values of the statistical measures for data with statistical moments are less as compared to simple data and normalized data which indicates that the performance of the feed forward back propagation neural network (FFBPNN) on data with statistical moments is better when compared to simple data and normalized data.

A Review on Energy Consumption Optimization Techniques in IoT Based Smart Building Environments

In recent years, due to the unnecessary wastage of electrical energy in residential buildings, the requirement of energy optimization and user comfort has gained vital importance. In the literature, various techniques have been proposed addressing the energy optimization problem. The goal of each technique is to maintain a balance between user comfort and energy requirements, such that the user can achieve the desired comfort level with the minimum amount of energy consumption. Researchers have addressed the issue with the help of different optimization algorithms and variations in the parameters to reduce energy consumption. To the best of our knowledge, this problem is not solved yet due to its challenging nature. The gaps in the literature are due to advancements in technology, the drawbacks of optimization algorithms, and the introduction of new optimization algorithms. Further, many newly proposed optimization algorithms have produced better accuracy on the benchmark instances but have not been applied yet for the optimization of energy consumption in smart homes. In this paper, we have carried out a detailed literature review of the techniques used for the optimization of energy consumption and scheduling in smart homes. Detailed discussion has been carried out on different factors contributing towards thermal comfort, visual comfort, and air quality comfort. We have also reviewed the fog and edge computing techniques used in smart homes.

Electric Energy Consumption Prediction by Deep Learning with State Explainable Autoencoder

As energy demand grows globally, the energy management system (EMS) is becoming increasingly important. Energy prediction is an essential component in the first step to create a management plan in EMS. Conventional energy prediction models focus on prediction performance, but in order to build an efficient system, it is necessary to predict energy demand according to various conditions. In this paper, we propose a method to predict energy demand in various situations using a deep learning model based on an autoencoder. This model consists of a projector that defines an appropriate state for a given situation and a predictor that forecasts energy demand from the defined state. The proposed model produces consumption predictions for 15, 30, 45, and 60 minutes with 60-minute demand to date. In the experiments with household electric power consumption data for five years, this model not only has a better performance with a mean squared error of 0.384 than the conventional models, but also improves the capacity to explain the results of prediction by visualizing the state with t-SNE algorithm. Despite unsupervised representation learning, we confirm that the proposed model defines the state well and predicts the energy demand accordingly.

Short-Term Forecasting of Total Energy Consumption for India-A Black Box Based Approach

Continual energy availability is one of the prime inputs requisite for the persistent growth of any country. This becomes even more important for a country like India, which is one of the rapidly developing economies. Therefore electrical energy’s short-term demand forecasting is an essential step in the process of energy planning. The intent of this article is to predict the Total Electricity Consumption (TEC) in industry, agriculture, domestic, commercial, traction railways and other sectors of India for 2030. The methodology includes the familiar black-box approaches for forecasting namely multiple linear regression (MLR), simple regression model (SRM) along with correlation, exponential smoothing, Holt’s, Brown’s and expert model with the input variables population, GDP and GDP per capita using the software used are IBM SPSS Statistics 20 and Microsoft Excel 1997–2003 Worksheet. The input factors namely GDP, population and GDP per capita were taken into consideration. Analyses were also carried out to find the important variables influencing the energy consumption pattern. Several models such as Brown’s model, Holt’s model, Expert model and damped trend model were analysed. The TEC for the years 2019, 2024 and 2030 were forecasted to be 1,162,453 MW, 1,442,410 MW and 1,778,358 MW respectively. When compared with Population, GDP per capita, it is concluded that GDP foresees TEC better. The forecasting of total electricity consumption for the year 2030–2031 for India is found to be 1834349 MW. Therefore energy planning of a country relies heavily upon precise proper demand forecasting. Precise forecasting is one of the major challenges to manage in the energy sector of any nation. Moreover forecasts are important for the effective formulation of energy laws and policies in order to conserve the natural resources, protect the ecosystem, promote the nation’s economy and protect the health and safety of the society.

Deep Highway Networks and Tree-Based Ensemble for Predicting Short-Term Building Energy Consumption

Predictive analytics play a significant role in ensuring optimal and secure operation of power systems, reducing energy consumption, detecting fault and diagnosis, and improving grid resilience. However, due to system nonlinearities, delay, and complexity of the problem because of many influencing factors (e.g., climate, occupants’ behaviour, occupancy pattern, building type), it is a challenging task to get accurate energy consumption prediction. This paper investigates the accuracy and generalisation capabilities of deep highway networks (DHN) and extremely randomized trees (ET) for predicting hourly heating, ventilation and air conditioning (HVAC) energy consumption of a hotel building. Their performance was compared with support vector regression (SVR), a most widely used supervised machine learning algorithm. Results showed that both ET and DHN models marginally outperform the SVR algorithm. The paper also details the impact of increasing the deep highway network’s complexity on its performance. The paper concludes that all developed models are equally applicable for predicting hourly HVAC energy consumption. Possible reasons for the minimum impact of DHN complexity and future research work are also highlighted in the paper.

Data Science and Big Data in Energy Forecasting

This editorial summarizes the performance of the special issue entitled Data Science and Big Data in Energy Forecasting, which was published at MDPI’s Energies journal. The special issue took place in 2017 and accepted a total of 13 papers from 7 different countries. Electrical, solar and wind energy forecasting were the most analyzed topics, introducing new methods with applications of utmost relevance.

Batch process fault detection and identification based on discriminant global preserving kernel slow feature analysis

As an attractive nonlinear dynamic data analysis tool, global preserving kernel slow feature analysis (GKSFA) has achieved great success in extracting the high nonlinearity and inherently time-varying dynamics of batch process. However, GKSFA is an unsupervised feature extraction method and lacks the ability to utilize batch process class label information, which may not offer the most effective means for dealing with batch process monitoring. To overcome this problem, we propose a novel batch process monitoring method based on the modified GKSFA, referred to as discriminant global preserving kernel slow feature analysis (DGKSFA), by closely integrating discriminant analysis and GKSFA. The proposed DGKSFA method can extract discriminant feature of batch process as well as preserve global and local geometrical structure information of observed data. For the purpose of fault detection, a monitoring statistic is constructed based on the distance between the optimal kernel feature vectors of test data and normal data. To tackle the challenging issue of nonlinear fault variable identification, a new nonlinear contribution plot method is also developed to help identifying the fault variable after a fault is detected, which is derived from the idea of variable pseudo-sample trajectory projection in DGKSFA nonlinear biplot. Simulation results conducted on a numerical nonlinear dynamic system and the benchmark fed-batch penicillin fermentation process demonstrate that the proposed process monitoring and fault diagnosis approach can effectively detect fault and distinguish fault variables from normal variables.

Multi-Objectives Optimization of Ventilation Controllers for Passive Cooling in Residential Buildings

An inappropriate indoor climate, mostly indoor temperature, may cause occupants’ discomfort. There are a great number of air conditioning systems that make it possible to maintain the required thermal comfort. Their installation, however, involves high investment costs and high energy demand. The study analyses the possibilities of limiting too high a temperature in residential buildings using passive cooling by means of ventilation with ambient cool air. A fuzzy logic controller whose aim is to control mechanical ventilation has been proposed and optimized. In order to optimize the controller, the modified Multiobjective Evolutionary Algorithm, based on the Strength Pareto Evolutionary Algorithm, has been adopted. The optimization algorithm has been implemented in MATLAB^®, which is coupled by MLE+ with EnergyPlus for performing dynamic co-simulation between the programs. The example of a single detached building shows that the occupants’ thermal comfort in a transitional climate may improve significantly owing to mechanical ventilation controlled by the suggested fuzzy logic controller. When the system is connected to the traditional cooling system, it may further bring about a decrease in cooling demand.

Big Data Analytics for Discovering Electricity Consumption Patterns in Smart Cities

New technologies such as sensor networks have been incorporated into the management of buildings for organizations and cities. Sensor networks have led to an exponential increase in the volume of data available in recent years, which can be used to extract consumption patterns for the purposes of energy and monetary savings. For this reason, new approaches and strategies are needed to analyze information in big data environments. This paper proposes a methodology to extract electric energy consumption patterns in big data time series, so that very valuable conclusions can be made for managers and governments. The methodology is based on the study of four clustering validity indices in their parallelized versions along with the application of a clustering technique. In particular, this work uses a voting system to choose an optimal number of clusters from the results of the indices, as well as the application of the distributed version of the k-means algorithm included in Apache Spark’s Machine Learning Library. The results, using electricity consumption for the years 2011–2017 for eight buildings of a public university, are presented and discussed. In addition, the performance of the proposed methodology is evaluated using synthetic big data, which cab represent thousands of buildings in a smart city. Finally, policies derived from the patterns discovered are proposed to optimize energy usage across the university campus.

Big Data Mining of Energy Time Series for Behavioral Analytics and Energy Consumption Forecasting

Responsible, efficient and environmentally aware energy consumption behavior is becoming a necessity for the reliable modern electricity grid. In this paper, we present an intelligent data mining model to analyze, forecast and visualize energy time series to uncover various temporal energy consumption patterns. These patterns define the appliance usage in terms of association with time such as hour of the day, period of the day, weekday, week, month and season of the year as well as appliance-appliance associations in a household, which are key factors to infer and analyze the impact of consumers’ energy consumption behavior and energy forecasting trend. This is challenging since it is not trivial to determine the multiple relationships among different appliances usage from concurrent streams of data. Also, it is difficult to derive accurate relationships between interval-based events where multiple appliance usages persist for some duration. To overcome these challenges, we propose unsupervised data clustering and frequent pattern mining analysis on energy time series, and Bayesian network prediction for energy usage forecasting. We perform extensive experiments using real-world context-rich smart meter datasets. The accuracy results of identifying appliance usage patterns using the proposed model outperformed Support Vector Machine (SVM) and Multi-Layer Perceptron (MLP) at each stage while attaining a combined accuracy of 81.82%, 85.90%, 89.58% for 25%, 50% and 75% of the training data size respectively. Moreover, we achieved energy consumption forecast accuracies of 81.89% for short-term (hourly) and 75.88%, 79.23%, 74.74%, and 72.81% for the long-term; i.e., day, week, month, and season respectively.