Hybrid Optimization based on Deep Learning Approach for Short-Term Load Forecast of Electricity Demand in Buildings - Charan Sekhar Makula; Ratna Dahiya (Majlesi Journal of Electrical Engineering, 2024)
Due to the growing popularity of microgrids in buildings, the foreseeable electricity demand for a building draws the attention of many researchers. The precise short-term demand forecast efficiently directs building managers and operators for interactions with electrical distribution systems, daily operational decisions, and energy conservation. This research proposes a hybrid optimization-based deep learning (DL) approach to increase the accuracy of short-term forecasts. The present work employs the Bilateral Long Short-Term Memory (BiLSTM) network-based DL technique because the BiLSTM technique has an exceptional ability to manage nonlinear interactions in data and learn the temporal dependencies. The performance of the BiLSTM technique is improved by using the optimally determined hyperparameters via a hybrid optimization algorithm that combines particle swarm optimization (PSO) and grey wolf optimization (GWO). The exploration ability of GWO and exploitation ability of PSO are effectively combined in the hybrid optimization GWO-PSO. The performance of the recommended approach is assessed using a case study of an educational building. The performance of the proposed model is compared to existing nonoptimal BiLSTM and single optimization-based BiLSTM for short-term load forecast.