A Survey of Computational Intelligence Techniques for Wind Power Uncertainty Quantification in Smart Grids

Probabilistic Forecasting With Modified N-BEATS Networks

In this article, we present a modification to the state-of-the-art N-BEATS deep learning architecture for the univariate time series point forecasting problem for generating parametric probabilistic forecasts. Next, we propose an extension to this probabilistic N-BEATS architecture to allow optimizing probabilistic forecasts from both a traditional forecast accuracy perspective as well as a forecast stability perspective, where the latter is defined in terms of a change in the forecast distribution for a specific time period caused by updating the probabilistic forecast for this time period when new observations become available (i.e., as time passes). We empirically show that this extension leads to more stable forecast distributions without causing considerable losses in forecast accuracy for the M4 monthly dataset. Finally, we present a second extension to the probabilistic N-BEATS network which makes it possible to jointly optimize single-period marginal and multiperiod cumulative (i.e., aggregated over multiple time periods) probabilistic forecasts. Empirical results are reported for the M4 monthly dataset and indicate that improvements in accuracy can be obtained over basic but well-established methods to produce probabilistic cumulative forecasts. The proposed probabilistic N-BEATS network and the extensions are all useful in a supply chain planning context.

Deep Into the Domain Shift: Transfer Learning Through Dependence Regularization

Classical domain adaptation methods acquire transferability by regularizing the overall distributional discrepancies between features in the source domain (labeled) and features in the target domain (unlabeled). They often do not differentiate whether the domain differences come from the marginals or the dependence structures. In many business and financial applications, the labeling function usually has different sensitivities to the changes in the marginals versus changes in the dependence structures. Measuring the overall distributional differences will not be discriminative enough in acquiring transferability. Without the needed structural resolution, the learned transfer is less optimal. This article proposes a new domain adaptation approach in which one can measure the differences in the internal dependence structure separately from those in the marginals. By optimizing the relative weights among them, the new regularization strategy greatly relaxes the rigidness of the existing approaches. It allows a learning machine to pay special attention to places where the differences matter the most. Experiments on three real-world datasets show that the improvements are quite notable and robust compared to various benchmark domain adaptation models.

Optimal dispatching of regional power grid considering vehicle network interaction

When large-scale electric vehicles are connected to the grid for unordered charging, it will seriously affect the stability and security of the power system. To solve this problem, this paper proposes a regional power network optimization scheduling method considering vehicle network interaction. Initially, based on the user behavior characteristics and charging and discharging characteristics of electric vehicles, a charging and discharging behavior model of electric vehicles was established. Based on the Monte Carlo sampling algorithm, the scheduling upper and lower limits of each scheduling cycle of electric vehicles were described, and the scheduling potential of each scheduling cycle of electric vehicles was obtained. Then, the electricity price is then used as an incentive parameter to guide EV users to charge during periods of low electricity prices and participate in discharge during periods of peak electricity prices. Aiming at the highest economic efficiency, the best consumption effect of new energy and the smoothest demand-side power curve of regional power grid, a three-objective optimal dispatching model was established. In the later stage, uncertainty factors are taken into consideration by introducing the concept of interval numbers, and an interval multi-objective optimization dispatching model is established. The two dispatching models are solved by NSGA-II algorithm and improved NSGA-II algorithm, and the Pareto solution set is obtained. Finally, based on the analytic Hierarchy Process (AHP), the optimal scheduling scheme is determined. The Monte Carlo sampling method is used to simulate the user side charging demand, and the effectiveness of this method is verified. In addition, the results of the interval multi-objective optimization model and the deterministic multi-objective optimization model are compared, and it is proved that the solution results of the interval multi-objective model are more adaptive, practical and robust to the uncertain factors.

Deep Fuzzy Min-Max Neural Network: Analysis and Design

Fuzzy min-max neural network (FMNN) is one kind of three-layer models based on hyperboxes that are constructed in a sequential way. Such a sequential mechanism inevitably leads to the input order and overlap region problem. In this study, we propose a deep FMNN (DFMNN) based on initialization and optimization operation to overcome these limitations. Initialization operation that can solve the input order problem is to design hyperboxes in a simultaneous way, and side parameters have been proposed to control the size of hyperboxes. Optimization operation that can eliminate overlap region problem is realized by means of deep layers, where the number of layers is immediately determined when the overlap among hyperboxes is eliminated. In the optimization process, each layer consists of three sections, namely, the partition section, combination section, and union section. The partition section aims to divide the hyperboxes into a nonoverlapping hyperbox set and an overlapping hyperbox set. The combination section eliminates the overlap problem of overlapping hyperbox set. The union section obtains the optimized hyperbox set in the current layer. DFMNN is evaluated based on a series of benchmark datasets. A comparative analysis illustrates that the proposed DFMNN model outperforms several models previously reported in the literature.

Distributed Energy Trading and Scheduling Among Microgrids via Multiagent Reinforcement Learning

Renewable energy technologies empower microgrids to generate electricity to supply themselves and trade with others. Under this paradigm, microgrids have become autonomous entities that must intelligently determine their policies for energy trading and scheduling. Many factors influence a microgrid's decision-making, such as the complex microgrid infrastructure, the uncertain energy yield and demand, and the competition among the energy market players. These factors are usually hard to precisely model, and deriving the optimal policy for a microgrid is challenging. We propose a multiagent reinforcement learning (MARL) approach with an attention mechanism to learn the optimal policies for the microgrids without complex system modeling. We model each microgrid as an autonomous agent, which learns how to schedule energy resources and trade with others by collaborating with other agents. We adopt attention mechanism to enable intelligently selecting contextual information for the training of each agent. After training, an agent can make control decisions using only its local information, which can well preserve the microgrids' privacy and reduce the communication overhead among microgrids to facilitate distributed control. We implement a simulation environment and evaluate the performances of our proposed method using real-world datasets. The experimental results show that our method can significantly reduce the cost of the microgrids compared with the baseline methods.

Perception and Navigation in Autonomous Systems in the Era of Learning: A Survey

Autonomous systems possess the features of inferring their own state, understanding their surroundings, and performing autonomous navigation. With the applications of learning systems, like deep learning and reinforcement learning, the visual-based self-state estimation, environment perception, and navigation capabilities of autonomous systems have been efficiently addressed, and many new learning-based algorithms have surfaced with respect to autonomous visual perception and navigation. In this review, we focus on the applications of learning-based monocular approaches in ego-motion perception, environment perception, and navigation in autonomous systems, which is different from previous reviews that discussed traditional methods. First, we delineate the shortcomings of existing classical visual simultaneous localization and mapping (vSLAM) solutions, which demonstrate the necessity to integrate deep learning techniques. Second, we review the visual-based environmental perception and understanding methods based on deep learning, including deep learning-based monocular depth estimation, monocular ego-motion prediction, image enhancement, object detection, semantic segmentation, and their combinations with traditional vSLAM frameworks. Then, we focus on the visual navigation based on learning systems, mainly including reinforcement learning and deep reinforcement learning. Finally, we examine several challenges and promising directions discussed and concluded in related research of learning systems in the era of computer science and robotics.

A real-time prediction interval correction method with an unscented Kalman filter for settlement monitoring of a power station dam

A prediction interval (PI) method is developed to quantify the model uncertainty of embankment settlement prediction. Traditional PIs are constructed based on specific past period information and remain unchanged; hence, they neglect discrepancies between previous calculations and new monitoring data. In this paper, a real-time prediction interval correction method is proposed. Time-varying PIs are built by continuously incorporating new measurements into model uncertainty calculations. The method consists of trend identification, PI construction, and real-time correction. Primarily, trend identification is carried out by wavelet analysis to eliminate early unstable noise and determine the settlement trend. Then, the Delta method is applied to construct PIs based on the characterized trend, and a comprehensive evaluation index is introduced. The model output and the upper and lower bounds of the PIs are updated by the unscented Kalman filter (UKF). The effect of the UKF is compared with that of the Kalman filter (KF) and extended Kalman filter (EKF). The method was demonstrated in the Qingyuan power station dam. The results show that the time-varying PIs based on trend data are smoother than those based on original data with better evaluation index scores. Also, the PIs are not affected by local anomalies. The proposed PIs are consistent with the actual measurements, and the UKF performs better than the KF and EKF. The approach has the potential to provide more reliable embankment safety assessments.

An enhancement of transformer-based architecture with randomized regularization for wind speed prediction

The wind power is considered as a potential renewable energy resource which requires less management cost and effort than the others like as tidal, geothermal, etc. However, the natural randomization and volatility aspects of wind in different regions have brought several challenges for efficiently as well as reliably operating the wind-based power supply grid. Thus, it is necessary to have centralized monitoring centers for managing as well as optimizing the performance of wind power farms. Among different management task, wind speed prediction is considered as an important task which directly support for further wind-based power supply resource planning/optimization, hence towards power shortage risk and operating cost reductions. Normally, considering as traditional time-series based prediction problem, most of previous deep learning-based models have demonstrated significant improvement in accuracy performance of wind speed prediction problem. However, most of recurrent neural network (RNN) as well as sequential auto-encoding (AE) based architectures still suffered several limitations related to the capability of sufficient preserving the spatiotemporal and long-range time dependent information of complex time-series based wind datasets. Moreover, previous RNN-based wind speed predictive models also perform poor prediction results within high-complex/noised time-series based wind speed datasets. Thus, in order to overcome these limitations, in this paper we proposed a novel integrated convolutional neural network (CNN)-based spatiotemporal randomization mechanism with transformer-based architecture for wind speed prediction problem, called as: RTrans-WP. Within our RTrans-WP model, we integrated the deep neural encoding component with a randomized CNN learning mechanism to softy align temporal feature within the long-range time-dependent learning context. The utilization of randomized CNN component at the data encoding part also enables to reduce noises and time-series based observation uncertainties which are occurred during the data representation learning and wind speed prediction-driven fine-tuning processes.

Forecasting renewable energy for environmental resilience through computational intelligence

Wind power forecasting plays a key role in the design and maintenance of wind power generation which can directly help to enhance environment resilience. Offshore wind power forecasting has become more challenging due to their operation in a harsh and multi-faceted environment. In this paper, the data generated from offshore wind turbines are used for power forecasting purposes. First, fragmented data is filtered and Deep Auto-Encoding is used to select high dimensional features. Second, a mixture of the CNN and LSTM models is used to train prominent wind features and further improve forecasting accuracy. Finally, the CNN-LSTM deep learning hybrid model is fine-tuned with various parameters for reliable forecasting of wind energy on three different offshore Windfarms. A state-of-the-art comparison against existing models is presented based on root mean square error (RMSE) and mean absolute error (MAE) respectively. The forecasting analyses indicate that the proposed CNN-LSTM strategy is quite successful for offshore wind turbines by retaining the lowest RMSE and MAE along with high forecasting accuracy. The experimental findings will be helpful to design environment resilient energy transition pathways.

Outage Estimation in Electric Power Distribution Systems Using a Neural Network Ensemble

Outages in an overhead power distribution system are caused by multiple environmental factors, such as weather, trees, and animal activity. Since they form a major portion of the outages, the ability to accurately estimate these outages is a significant step towards enhancing the reliability of power distribution systems. Earlier research with statistical models, neural networks, and committee machines to estimate weather-related and animal-related outages has reported some success. In this paper, a deep neural network ensemble model for outage estimation is proposed. The entire input space is partitioned with a distinct neural network in the ensemble performing outage estimate in each partition. A novel algorithm is proposed to train the neural networks in the ensemble, while simultaneously partitioning the input space in a suitable manner. The proposed approach has been compared with the earlier approaches for outage estimation for four U.S. cities. The results suggest that the proposed method significantly improves the estimates of outages caused by wind and lightning in power distribution systems. A comparative analysis with a previously published model for animal-related outages further establishes the overall effectiveness of the deep neural network ensemble.

An Uncertainty-Aware Transfer Learning-Based Framework for COVID-19 Diagnosis

The early and reliable detection of COVID-19 infected patients is essential to prevent and limit its outbreak. The PCR tests for COVID-19 detection are not available in many countries, and also, there are genuine concerns about their reliability and performance. Motivated by these shortcomings, this article proposes a deep uncertainty-aware transfer learning framework for COVID-19 detection using medical images. Four popular convolutional neural networks (CNNs), including VGG16, ResNet50, DenseNet121, and InceptionResNetV2, are first applied to extract deep features from chest X-ray and computed tomography (CT) images. Extracted features are then processed by different machine learning and statistical modeling techniques to identify COVID-19 cases. We also calculate and report the epistemic uncertainty of classification results to identify regions where the trained models are not confident about their decisions (out of distribution problem). Comprehensive simulation results for X-ray and CT image data sets indicate that linear support vector machine and neural network models achieve the best results as measured by accuracy, sensitivity, specificity, and area under the receiver operating characteristic (ROC) curve (AUC). Also, it is found that predictive uncertainty estimates are much higher for CT images compared to X-ray images.