Load forecasting based on grasshopper optimization and a multilayer feed-forward neural network using regressive approach

Manta ray foraging optimization algorithm–based feedforward neural network for electric energy consumption forecasting

“…Since the GOA increased the convergence rate, the proposed MFFNN-GOA performed better than the other compared methods. 19 In another study, Singh et al focused on investigating the performance of the hybrid ANN-IEAMCGM-R for short-term load forecasting. The ANN was integrated with the IEAMCGM, an advanced evolutionary algorithm, to find the optimum network weights.…”

“…The performance of the proposed model was compared with the GA‐based MFFNN (MFFNN‐GA), gray wolf optimization (GWO)‐based MFFNN (MFFNN‐GWO), and classical MFFNN. Since the GOA increased the convergence rate, the proposed MFFNN‐GOA performed better than the other compared methods 19 . In another study, Singh et al focused on investigating the performance of the hybrid ANN‐IEAMCGM‐R for short‐term load forecasting.…”

HSIC Bottleneck Based Distributed Deep Learning Model for Load Forecasting in Smart Grid With a Comprehensive Survey

“…Some works focused on multiple load forecasting processes. In one, FFNN, DNN, Grasshopper Optimization algorithm-based load forecasting has been developed for MTLF and STLF [42]. In another work, Neuro-evolution and RNN based load forecasting was applied for both VSTLF and STLF [133].…”

“…In a few cases, MI-ANN, WNN, GRU, DBN, RBM, ANFIS, and ART network approaches were applied to obtain better performances. Cascade NN, KNN-ANN [44], [48], [63], [65], [66], [73], [74], [75], [96], [109], [124], [129], [131], [133], [134], [137], [ [40], [42], [45] - [65], [67] - [73], [76] - [95], [97], [98], [101] - [103], [105] - [107], [110] - [119], [121], [123] - [126], [130] - [41], [42], [45] - [48], [52], [57], [61] - [63], [65], [69], [70], [72], [76],…”

An attention‐based CNN‐LSTM‐BiLSTM model for short‐term electric load forecasting in integrated energy system

“…For IES, the connection of cooling (heat) consumption, natural gas and solar energy also need to be taken into account. Because of the time sequence, nonlinearity of power load, and the power system is greatly affected by the demand side, it is very difficult to forecast the long‐term electric load of the power system 4 . Most researches focus on short‐term load forecasting of power system.…”