Bio-Inspired Bidirectional Deep Machine Learning for Real-Time Energy Consumption Forecasting and Management

Abstract

Accurately predicting electrical power demand is crucial to making related forecasts and to effective sustainable energy management. Most relevant state-of-the-art studies deploy models that do not use optimizing parameters and do not incorporate strategies for using forecast results. This study was designed to develop a novel electricity consumption forecasting model, the Symbiotic Bidirectional Gated Recurrent Unit, which integrates Gated Recurrent Unit, Bidirectional Technique, and Symbiotic Organisms Search algorithms. The results of tests on a series of evaluation criteria showed the proposed model performed significantly better than six comparison models when parameter optimization was used. For all three sector datasets, the proposed model generated the most-accurate predictions of all models. In practical terms, when supply is expected to exceed demand, the prediction results may be used to adjust power plant output to reduce wastage. Conversely, when demand is expected to exceed supply, Time-of-Use tariffs may be implemented based on time-of-day and seasonal fluctuations in demand to facilitate reductions in peak usage and level out overall demand.