Load forecasting based on grasshopper optimization and a multilayer feed-forward neural network using regressive approach M. Talaat a, c, * , M.A. Farahat a , Noura Mansour a , A.Y. Hatata b, c a Electrical Power and Machines, Faculty of Engineering, Zagazig University, P.O. 44519, Zagazig, Egypt b Electrical Engineering Department, Faculty of Engineering, Mansoura University, Egypt c Electrical Engineering Department, College of Engineering, Shaqra University, Dawadmi, Ar Riyadh, Saudi Arabia article info Article history: Received 18 September 2019 Received in revised form 15 December 2019 Accepted 2 February 2020 Available online 4 February 2020 Keywords: Mid-term load forecasting Short-term load forecasting Power generation Multilayer feed-forward neural network Grasshopper optimization algorithm Regressive model abstract This paper introduces a proposed model for mid-term to short-term load forecasting (MTLF; STLF) that can be used to forecast loads at different hours and on different days of each month. The combined MT- STLF model was investigated to aid in power generation and electricity purchase planning. A hybrid model of a multilayer feed-forward neural network (MFFNN) and the grasshopper optimization algo- rithm (GOA) was introduced to obtain high-accuracy results for load forecasting using the combined MT- STLF model. The MFFNN is prepared by processing the input layer and output layer and finally selecting a suitable number of hidden layers. The main steps in developing the model from the MFFNN include entering the data into the network, training the model and finally implementing the prediction process. The accuracy of the model obtained before using the GOA was lower than that after applying the GOA. Weather factors such as the temperature were used as inputs to the MFFNN during MT-STLF modelling to ensure high accuracy. In the proposed model, the temperature had a clear effect on the forecasted load. Additionally, there was a difference between the maximum and minimum loads in winter and summer months. A regressive model was introduced to determine the relations between the dependent variable (the load) and the independent variables that affect the load, such as the temperature. The regressive model used in the paper highlights the effect of the temperature on the hourly load. The accuracy of the hybrid model is satisfied with deviation error varied between 0.06 and 0.06. Moreover, the perfor- mance of the proposed forecasting model has been assessed by three indices; Root Mean Square Error (RMSE), Mean Absolute Error (MAE) and Mean Absolute Percentage Error (MAPE) then, compared with other forecasting models considering other optimization algorithms. © 2020 Elsevier Ltd. All rights reserved. 1. Introduction Recently, load forecasting has become a critical task in power system operations. The load refers to the consumption of power from power system plants [1]. The new trend in the sustainable energy development and entrepreneurship is to utilize the energy that has satisfied the human needs. This requires the knowledge of the energy forecasting to utilize the available energy by a smart way with smart decision. This energy forecasting is depending on the diagnosis of the data available from the power system grid Abbreviations: MTLF, Mid-Term Load Forecasting; STLF, Short-Term Load Forecasting; MT-STLF, Mid-Term to Short-Term Load Forecasting; MFFNN, Multilayer Feed- Forward Neural Network; GOA, Grasshopper Optimization Algorithm; LTLF, Long-Term Load Forecasting; ARIMA, Autoregressive Integrated Moving Average; AR, Auto Regression; MA, Moving Average; ARMA, Autoregressive Moving Average; ANN, Artificial Neural Network; GA, Genetic Algorithm; FI, Fuzzy Inference; SVM, Support Vector Machines; ELMs, Extreme Learning Machines; BNN, Bagging Neural Network; NN, Neural Networks; MLP, Multilayer Perceptron; RBFNN, Radial Basis Function Neural Network; GRNN, Generalized Regression Neural Network; CPNN, Counter-Propagation Neural Network; ENN, Elman Neural Network; WNN, Wavelet Neural Network; WT, Wavelet Transform; EMD, Empirical Mode Decomposition; GWO, Grey Wolf Optimization; ALO, Ant Lion Optimization; SVR, Support Vector Regression; ASO, Ant Swarm Optimization; ACO, Ant Colony Optimization; FFOA, Fruit Fly Optimization Algorithm; ABC, Artificial Bee Colony; RFA, Rainfall Algorithm; CSO, Chaotic Swarm Optimization; CSA, Cuckoo Search Algorithm; EAM, Environmental Adaptation Method; ML, Marquardt-Levenberg; MSE, Mean Square Error; MAE, Mean Absolute Error; RMSE, Root Mean Square Error; MAPE, Mean Absolute Percentage Error. * Corresponding author. Electrical Power and Machines, Faculty of Engineering, Zagazig University, P.O. 44519, Zagazig, Egypt. E-mail address: m_mtalaat@eng.zu.edu.eg (M. Talaat). Contents lists available at ScienceDirect Energy journal homepage: www.elsevier.com/locate/energy https://doi.org/10.1016/j.energy.2020.117087 0360-5442/© 2020 Elsevier Ltd. All rights reserved. Energy 196 (2020) 117087