Table 8 STLF techniques

STLF

2022

Hybrid method consisting of Prophet model, ARIMA (Autoregressive Integrated Moving Average) model, and LSTM model, and BPNN (Back Propagation Neural Network) [164]

Overcomes different technical gaps of load forecasting with low computational time and fast convergence

1. Complex calculation

2. High Calculation time

3. Expensive

4. Insufficient data

5. Clustering of data

6. Management of structured and unstructured data

Extreme Gradient Boosting (XGBoost) [165]

Forecasts loads specifically for warehouses and logistics consumption

Enhanced decision tree classifier (EDTC) [166]

Accurate prediction of the stability of the smart grid

Mix-up and transfer learning [167]

A reliable model for load forecasting designed for new houses

Integrated CEEMDAN-IGOA-LSTM (Complete Ensemble Empirical Mode Decomposition With Adaptive Noise, Improved Grasshopper Optimization Algorithm, and Long Short-Term Memory Network) [168]

Aggregates different data techniques to effectively forecast load

2021

CNN, RNN [169]

Works quickly and smoothly in noisy systems

Asynchronous Deep Deterministic Policy Gradient (ADDPG) Adaptive Early Forecasting (AEF) Reward Incentive Mechanism (RIM) model [170]

Solves the problem of excessive temporal connection and high convergence instability

Integrated CNN and LSTM Network [171]

Highly precise and accurate STLF which can analyze long sequence time-series data of electric load

2020

LSTM, Reinforcement learning, DQN, BPNN [172]

Similar day identification and selection based on reinforcement learning on BPNN

RBN, MI-ANN, Genetic Wind Driven Optimization (GWDO) [173]

Load forecasting for linear and non-linear power systems

Singular Spectrum Analysis(SSA), Fuzzy ARTMAP, Neuro-fuzzy, BP [174]

Reducing the cost of computational energy and data requirements

Ensemble Empirical Mode Decomposition(EEMD), Multivariable Linear Regression(MLR) [175]

Analyzing large datasets for electric load

Kalman Filtering, Clustering techniques, Weightless Neural Network (WNN) [176]

Use of different clustering techniques to cluster load forecasting data

ELM, Genetic Algorithm, Support Vector Machine, XBoost, decision Tree [177]

Tuning hyper parameter and extracting features for load forecasting

2019

BP, LSTM, CNN [178]

Using LTSM and CNN for coupling electric load

XG-Boost, Decision Tree, Support Vector Regression (SVR) BP, CNN [179]

Predicting load forecasting within the price of electricity

WaveNet, CNN, BP, LSTM [180]

Improving performance of different error detection of load forecasting

LSTM, Ensemble learning, Quantile forecasting, Quantile method, ENN, Parallel computing [181]

Diminishing the need for feature extraction in load forecasting

Unsupervised Learning, BP, Auto Encoders, Denoising Autoencoders [182]

Error reduction for unsupervised load forecasting

Dropout technique, Fuzzy logic, CNN [183]

Feature extraction improvement with high accuracy and the over-fitting issue resolved

WaveNet, CNN, BP, LSTM [180]

Improving performance of different error detection of load forecasting

2018

SVR, Auto Encoders, Denoising Autoencoders [161]

Achieving high features of load forecasting from lower-level datasets