Hourly load prediction based feature selection scheme and hybrid CNN‐LSTM method for building's smart solar microgrid

• Published in: Expert systems Vol. 41; no. 7
• Main Authors: Da, Thao Nguyen, Cho, Ming‐Yuan, Thanh, Phuong Nguyen
• Format: Journal Article
• Language: English
• Published: Oxford Blackwell Publishing Ltd 01.07.2024
• Subjects: Algorithms; Artificial neural networks; Buildings; convolution neural network algorithm; Correlation analysis; Data collection; Deep learning; deep learning machine; Distributed generation; Electric power demand; Electrical loads; Energy management; Feature selection; feature selection scheme; gated recurrent unit; hyperparameter optimization; load prediction; long short‐term memory methodology; Parameters; Peak load; Power management; recurrent neural network
• ISSN: 0266-4720; 1468-0394
• DOI: 10.1111/exsy.13539

The short‐term load prediction is the critical operation in the peak demand administration and power generation scheduling of buildings that integrated the smart solar microgrid (SSM). Many research studies have proved that hybrid deep learning strategies achieve more accuracy and feasibility in practical applications than individual algorithms. Moreover, many buildings have integrated the SSM on the rooftop with the battery management system (BMS) to enhance energy efficiency management. However, the traditional methodologies only processed the weather parameters and power demand information for short‐term load prediction, ignoring the collected data from SSM and BMS by the advanced metering infrastructures (AMI), which probably improved prediction accuracy. In this research, many accumulated data of building and SSM are collected before methodology implementation. Considering the diversities of accumulated parameters from SSM and BMS, an adaptive convolution neural network long short‐term memory (CNN‐LSTM) is proposed for hourly electrical load prediction. The CNN could extract the critical large‐scale input feature, while the LSTM could achieve better accurate forecasts. The Pearson correlation matrix is calculated for the feature selection scheme from different data units. The hyperparameter tuning is utilized for obtaining the optimized hybrid CNN‐LSTM algorithm. The K‐fold cross‐validation is employed for deep learning algorithm verification, which includes LSTM, GRU, CNN, and Bi‐LSTM methodologies. The results prove that the hybrid CNN‐LSTM achieved outperformed improvements, which are 20.57%, 29.63%, 19.06% in MSE, MAE, MAPE, and 21.24%, 22.02%, 3.82% in validating MSE, MAE, MAPE, respectively. The hybrid CNN and LSTM combined with the feature selection scheme achieve superior predicting accuracies, proving the adaptability ability for integrating into the energy management system (EMS) of the building's SSM.