Forecasting Energy Consumption Using a Novel Hybrid Dipper Throated Optimization and Stochastic Fractal Search Algorithm

Abstract

The accurate prediction of energy consumption has effective role in decision making and risk management for individuals and governments. Meanwhile, the accurate prediction can be realized using the recent advances in machine learning and predictive models. This research proposes a novel approach for energy consumption forecasting based on a new optimization algorithm and a new forecasting model consisting of a set of long short-term memory (LSTM) units. The proposed optimization algorithm is used to optimize the parameters of the LSTM-based model to boost its forecasting accuracy. This optimization algorithm is based on the recently emerged dipper-throated optimization (DTO) and stochastic fractal search (SFS) algorithm and is referred to as dynamic DTOSFS. To prove the effectiveness and superiority of the proposed approach, five standard benchmark algorithms, namely, stochastic fractal search (SFS), dipper throated optimization (DTO), whale optimization algorithm (WOA), particle swarm optimization (PSO), and grey wolf optimization (GWO), are used to optimize the parameters of the LSTM-based model, and the results are compared with that of the proposed approach. Experimental results show that the proposed DDTOSFS + LSTM can accurately forecast the energy consumption with root mean square error RMSE of 0.00013, which is the best among the recorded results of the other methods. In addition, statistical experiments are conducted to prove the statistical difference of the proposed model. The results of these tests confirmed the expected outcomes.