Short-Term Load Forecasting Model of Electric Vehicle Charging Load Based on MCCNN-TCN

A hybrid Bayesian network-based deep learning approach combining climatic and reliability factors to forecast electric vehicle charging capacity

The increasing adoption of electric vehicles (EVs) necessitates advanced predictive models to accurately forecast charging demand and ensure reliable infrastructure planning. This study introduces a novel analytical framework that integrates queuing network and Bayesian network models to enhance the prediction accuracy and reliability of EV charging demand. The objective is to develop a comprehensive system that accounts for various influencing factors, such as meteorological conditions and charging pile failure rates, to optimize EV infrastructure. The methodology involves creating a hybrid Bayesian Network-based deep learning (HBNDL) system architecture. This architecture uses extensive transaction data and climate analysis to build a detailed model of EV charging pile reliability. Additionally, two algorithms are designed to assess the usage and reliability of charging stations. The framework's effectiveness is tested through a series of experiments evaluating its performance in short-, medium-, and long-term prediction scenarios. The results demonstrate that the HBNDL framework significantly improves prediction accuracy and infrastructure reliability. The integration of queuing theory and Bayesian network models with deep learning techniques results in a robust system adaptable to various conditions. Experimental validation shows that the proposed framework outperforms existing models in forecasting EV charging demand, particularly under varying environmental influences.

Multi-Step Ahead Short-Term Electricity Load Forecasting Using VMD-TCN and Error Correction Strategy

The electricity load forecasting plays a pivotal role in the operation of power utility companies precise forecasting and is crucial to mitigate the challenges of supply and demand in the smart grid. More recently, the hybrid models combining signal decomposition and artificial neural networks have received popularity due to their applicability to reduce the difficulty of prediction. However, the commonly used decomposition algorithms and recurrent neural network-based models still confront some dilemmas such as boundary effects, time consumption, etc. Therefore, a hybrid prediction model combining variational mode decomposition (VMD), a temporal convolutional network (TCN), and an error correction strategy is proposed. To address the difficulty in determining the decomposition number and penalty factor for VMD decomposition, the idea of weighted permutation entropy is introduced. The decomposition hyperparameters are optimized by using a comprehensive indicator that takes account of the complexity and amplitude of the subsequences. Besides, a temporal convolutional network is adopted to carry out feature extraction and load prediction for each subsequence, with the primary forecasting results obtained by combining the prediction of each TCN model. In order to further improve the accuracy of prediction for the model, an error correction strategy is applied according to the prediction error of the train set. The Global Energy Competition 2014 dataset is employed to demonstrate the effectiveness and practicality of the proposed hybrid model. The experimental results show that the prediction performance of the proposed hybrid model outperforms the contrast models. The accuracy achieves 0.274%, 0.326%, and 0.405 for 6-steps, 12-steps, and 24 steps ahead forecasting, respectively, in terms of the mean absolute percentage error.

Situational Awareness for Smart Distribution Systems

In recent years, the accelerating climate change and intensifying natural disasters have called for more renewable, resilient, and reliable energy from more distributed sources to more diversified consumers, resulting in a pressing need for advanced situational awareness of modern smart distribution systems [...]