Four-Objective Optimizations of a Single Resonance Energy Selective Electron Refrigerator

According to the established model of a single resonance energy selective electron refrigerator with heat leakage in the previous literature, this paper performs multi-objective optimization with finite-time thermodynamic theory and NSGA-II algorithm. Cooling load (R¯), coefficient of performance (ε), ecological function (ECO¯), and figure of merit (χ¯) of the ESER are taken as objective functions. Energy boundary (E′/kB) and resonance width (ΔE/kB) are regarded as optimization variables and their optimal intervals are obtained. The optimal solutions of quadru-, tri-, bi-, and single-objective optimizations are obtained by selecting the minimum deviation indices with three approaches of TOPSIS, LINMAP, and Shannon Entropy; the smaller the value of deviation index, the better the result. The results show that values of E′/kB and ΔE/kB are closely related to the values of the four optimization objectives; selecting the appropriate values of the system can design the system for optimal performance. The deviation indices are 0.0812 with LINMAP and TOPSIS approaches for four-objective optimization (ECO¯−R¯−ε−χ¯), while the deviation indices are 0.1085, 0.8455, 0.1865, and 0.1780 for four single-objective optimizations of maximum ECO¯, R¯, ε, and χ¯, respectively. Compared with single-objective optimization, four-objective optimization can better take different optimization objectives into account by choosing appropriate decision-making approaches. The optimal values of E′/kB and ΔE/kB range mainly from 12 to 13, and 1.5 to 2.5, respectively, for the four-objective optimization.

Building Heating and Cooling Load Prediction Using Ensemble Machine Learning Model

Building energy consumption prediction has become an important research problem within the context of sustainable homes and smart cities. Data-driven approaches have been regarded as the most suitable for integration into smart houses. With the wide deployment of IoT sensors, the data generated from these sensors can be used for modeling and forecasting energy consumption patterns. Existing studies lag in prediction accuracy and various attributes of buildings are not very well studied. This study follows a data-driven approach in this regard. The novelty of the paper lies in the fact that an ensemble model is proposed, which provides higher performance regarding cooling and heating load prediction. Moreover, the influence of different features on heating and cooling load is investigated. Experiments are performed by considering different features such as glazing area, orientation, height, relative compactness, roof area, surface area, and wall area. Results indicate that relative compactness, surface area, and wall area play a significant role in selecting the appropriate cooling and heating load for a building. The proposed model achieves 0.999 R2 for heating load prediction and 0.997 R2 for cooling load prediction, which is superior to existing state-of-the-art models. The precise prediction of heating and cooling load, can help engineers design energy-efficient buildings, especially in the context of future smart homes.

The Significance of Occupants' Interaction with Their Environment on Reducing Cooling Loads and Dermatological Distresses in East Mediterranean Climates

Global endeavors to respond to the problems caused by climate change and are leading to higher temperatures inside homes, which can cause skin conditions (such as eczema), lethargy, and poor concentration; disturbed sleep and fatigue are also rising. The energy performance of buildings is influenced by interactions and associations of numerous different variables, such as the envelope specifications as well as the design, technologies, apparatuses, and occupant behaviours. This paper introduces simple and sustainable strategies that are not dependent on expensive or sophisticated technologies, as they rely only on the actions practiced by the building’s occupants (movable window shading, and nighttime natural ventilation) instead of completely relying on high-cost mechanical cooling systems in buildings located in the main Eastern Mediterranean climates represented in the country of Jordan. These low-energy solutions could be applied to low-income houses in hot areas to avoid health problems, such as dermatological diseases, and save a significant amount of energy. The final results indicate that window shading has significant potential in reducing the cooling load in different climate zones. Natural ventilation exhibits high energy-saving abilities in climates that have cool nights, whereas its abilities in hot climates where nights are moderate is limited.

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.

Thermal Effectiveness Enhancement of Autoclaved Aerated Concrete Wall with PCM-Contained Conical Holes to Reduce the Cooling Load

This work investigates and improves the thermal dynamics of autoclaved aerated concrete (AAC) wall containing phase change material (PCM). The PCM is paraffin wax loaded into conical holes drilled into the AAC. Filled AAC with three different numbers of PCM-filled holes (2, 3, and 4 conical holes, which are designated as AAC-2H, AAC-3H, and AAC-4H, respectively) as well as the unfilled original AAC were both tested under two different conditions: indoors (with controlled temperature) and outdoors (with actual weather). For the indoor experiment, a heater was used as a thermal source and set up to maintain the testing temperature at one of three levels: 40 °C, 50 °C, or 60 °C. The wall temperature was then measured on the surface with each horizontally-positioned wall as well as four different positions at various depths below the surface of the wall. It was found that AAC-4H was the optimum condition, which can produce outstandingly a time lag of approximately 27%, reduce a decrement factor of approximately 31%, and also decrease the room temperature. This reached approximately 9% when compared with that of ordinary AAC at the controlled testing temperature of 60 °C. All samples were further tested in actual weather to confirm the thermal performances of AAC-4H. Thermal effectiveness of AAC-4H was improved by extending approximately a 14.3% time lag, which reduces approximately a 4.3% decrement factor and achieving approximately 5% lower room temperature when compared with ordinary AAC.