Electrical Energy Prediction in Residential Buildings for Short-Term Horizons Using Hybrid Deep Learning Strategy

• Published in: Applied sciences Vol. 10; no. 23; p. 8634
• Main Authors: Khan, Zulfiqar Ahmad, Ullah, Amin, Ullah, Waseem, Rho, Seungmin, Lee, Miyoung, Baik, Sung Wook
• Format: Journal Article
• Language: English
• Published: Basel MDPI AG 01.12.2020
• Subjects: Accuracy; Artificial intelligence; bidirectional gated recurrent unit; convolutional neural networks; Datasets; Electricity; electricity consumption prediction; Electricity distribution; Energy consumption; Forecasting; hybrid deep learning model; Mean square errors; Methods; Neural networks; Power supply; residential load prediction; Sensors; United States > US
• DOI: 10.3390/app10238634
• ISSN: 2076-3417

Smart grid technology based on renewable energy and energy storage systems are attracting considerable attention towards energy crises. Accurate and reliable model for electricity prediction is considered a key factor for a suitable energy management policy. Currently, electricity consumption is rapidly increasing due to the rise in human population and technology development. Therefore, in this study, we established a two-step methodology for residential building load prediction, which comprises two stages: in the first stage, the raw data of electricity consumption are refined for effective training; and the second step includes a hybrid model with the integration of convolutional neural network (CNN) and multilayer bidirectional gated recurrent unit (MB-GRU). The CNN layers are incorporated into the model as a feature extractor, while MB-GRU learns the sequences between electricity consumption data. The proposed model is evaluated using the root mean square error (RMSE), mean square error (MSE), and mean absolute error (MAE) metrics. Finally, our model is assessed over benchmark datasets that exhibited an extensive drop in the error rate in comparison to other techniques. The results indicated that the proposed model reduced errors over the individual household electricity consumption prediction (IHEPC) dataset (i.e., RMSE (5%), MSE (4%), and MAE (4%)), and for the appliances load prediction (AEP) dataset (i.e., RMSE (2%), and MAE (1%)).