Table 9 VSTLF techniques

VSTLF

2022

ANN [184]

Load forecasting with optimal asset management

1. Weak performance on unstructured and

sparse data

2. Improper short time intervals

3. Insufficient data

4. High calculation time

5. Random and big data

6. Over-fitting problem

7. Management of structured and unstructured data

Extreme Gradient Boosting (XGBoost) [165]

Forecasts loads specifically for warehouses and logistics consumption

2021

Markov-chain mixture distribution (MCM) model [185]

Develops a standard model for household power consumption

2020

FFNN, Neuro-fuzzy, Fuzzy Multi-Objective Decision Making (F-MODM) [186]

Develops load forecasting 1 h ahead based on weather data

RNN, GRU, BP [187]

Predicting load demand of residential infrastructure for a short period

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

Reduces training time and testing time of load forecasting

CNN, Mutual Information (MI), MI-ANN, Relief F, Kernel Principal Component Analysis (KPCA), BP [189]

Over-fitting issue reduction and computational time reduction using CNN, KPCA, MI etc.

LTSM, Bayesian deep learning, Bayesian Theory [82]

Probabilistic-residential load forecasting for PV systems

2019

BPNN, Bayesian Regularization, Levenberg–Marquardt algorithm [190]

Load forecasting for individual district buildings

DBN, BP, Phase Space, Reconstruction PSR, Levenberg–Marquardt algorithm [191]

Predicting load forecasting of bus-load forecasting and distributed energy penetration

KNN-ANN, FFNN, Euclidean theory [192]

Load forecasting for hydro-thermal unit generation combining ANN and KNN

2018

Neuro-fuzzy, ANFIS, Genetic algorithm, Particle Swarm Optimization [193]

Decreasing execution or training time as well as reducing feature selection complexity