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

作　　者：Doaa Sami Khafaga El-Sayed M.El-kenawy Amel Ali Alhussan Marwa M.Eid 

机构地区：[1]Department of Computer Sciences,College of Computer and Information Sciences,Princess Nourah bint Abdulrahman University,P.O.Box 84428,Riyadh,11671,Saudi Arabia [2]Department of Communications and Electronics,Delta Higher Institute of Engineering and Technology,Mansoura,35111,Egypt [3]Faculty of Artificial Intelligence,Delta University for Science and Technology,Mansoura,Egypt

出　　处：《Intelligent Automation & Soft Computing》2023年第8期2117-2132,共16页智能自动化与软计算（英文）

基　　金：funded by the Deanship of Scientific Research,Princess Nourah bint Abdulrahman University,through the Program of Research Project Funding After Publication,Grant No (43-PRFA-P-52).

摘　　要：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)algo-rithm 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.

关 键 词：Stochastic fractal search dipper throated optimization energy consumption long short-term memory prediction models 

分 类 号：TP18[自动化与计算机技术—控制理论与控制工程]