Table 10 MTLF techniques

MTLF

2022

LSTM and NARX neural network [194]

Hourly energy demand prediction of a municipality

1. Over-fitting issue

2. Systems precision iii. Huge calculation time

SARIMA (seasonal auto-regressive integrated moving average) and ES (Exponential Smoothing) [195]

Predicts yearly consumption of electricity for the agriculture sector

ISSA-SVM (improved sparrow search algorithm-Support Vector Machine) [196]

Error index of load forecasting is kept optimal which results in better prediction accuracy

2021

LSTM network [197]

Load forecasting with minimal error for industrial power consumption

Support Vector Regression (SVR) [198]

Mean absolute percentage error (MAPE) and root mean square error (RMSE) are kept to a minimum

2020

BPNN, Singular Spectrum Analysis (SSA), Weightless Neural Network (WNN), Cuckoo Search algorithm [199]

Surveying load forecasting for wavelet disintegration to learn about the reduction of stochastic part

Grasshopper Optimization Algorithm, BP, Regressive Model [200]

Daily and hourly continuous load forecasting

Load Range Discretization (LRD), CNN, BP [201]

Probability distribution generation for load forecasting

Mutual Information-ANN, Jaya algorithm [202]

Removes feature selection redundancy

LSTM, Cascade NN, Edited Nearest Neighbor (ENN), Ensemble Learning. Levenberg–Marquardt algorithm [203]

Decreasing mean absolute percentage error by integrating cascade neural network in load forecasting

CNN, BP, Image encoding, Gramian Angular field, Recurrence Plot, Markov Transition field [204]

Single residential user load forecasting using CNN on time series datasets

DML, Apache Hadoop, Apache Spark, Linear Regression, Generalized Linear Regression, Decision Tree, Gradient-boosted trees, Random Forest, Distributed computing [188]

Development of a Distributed Machine Learning approach for reducing training time and test time with higher accuracy

2019

KNN-ANN, BPNN, Spark [205]

Handling multivariate data and multiple time series while predicting load forecasting outputs

LSTM, BPNN, Adaptive Moment Estimation [206]

Load forecasting prediction by analyzing electricity price

Parallel deep learning [207]

Ensuring control of hybrid energy storing system in a distributed system using parallel deep learning

LSTM, GRU [208]

Predicting load forecasting by training GRU and LTSM with various time scale sequences

2018

FFNN, Particle Swarm Optimization, MLP [209]

Mid-term load forecasting in terms of green environment and peak load

BPNN [210]

Identification of max power load at photovoltaic power generation and power capacity