Short-Term Load Forecasting Models: A Review of Challenges, Progress, and the Road Ahead DOI Creative Commons
Saima Akhtar, Sulman Shahzad,

Asad Zaheer

et al.

Energies, Journal Year: 2023, Volume and Issue: 16(10), P. 4060 - 4060

Published: May 12, 2023

Short-term load forecasting (STLF) is critical for the energy industry. Accurate predictions of future electricity demand are necessary to ensure power systems’ reliable and efficient operation. Various STLF models have been proposed in recent years, each with strengths weaknesses. This paper comprehensively reviews some models, including time series, artificial neural networks (ANNs), regression-based, hybrid models. It first introduces fundamental concepts challenges STLF, then discusses model class’s main features assumptions. The compares terms their accuracy, robustness, computational efficiency, scalability, adaptability identifies approach’s advantages limitations. Although this study suggests that ANNs may be most promising ways achieve accurate additional research required handle multiple input features, manage massive data sets, adjust shifting conditions.

Language: Английский

Taxonomy research of artificial intelligence for deterministic solar power forecasting DOI
Huaizhi Wang, Yangyang Liu, Bin Zhou

et al.

Energy Conversion and Management, Journal Year: 2020, Volume and Issue: 214, P. 112909 - 112909

Published: May 1, 2020

Language: Английский

Citations

266
Recent Advances in Machine Learning Research for Nanofluid-Based Heat Transfer in Renewable Energy System DOI
Prabhakar Sharma, Zafar Said,

Anurag Kumar

et al.

Energy & Fuels, Journal Year: 2022, Volume and Issue: 36(13), P. 6626 - 6658

Published: June 13, 2022

Nanofluids have gained significant popularity in the field of sustainable and renewable energy systems. The heat transfer capacity working fluid has a huge impact on efficiency system. addition small amount high thermal conductivity solid nanoparticles to base improves transfer. Even though large research data is available literature, some results are contradictory. Many influencing factors, as well nonlinearity refutations, make nanofluid highly challenging obstruct its potentially valuable uses. On other hand, data-driven machine learning techniques would be very useful for forecasting thermophysical features rate, identifying most influential assessing efficiencies different primary aim this review study look at applications employed nanofluid-based system, reveal new developments research. A variety modern algorithms studies systems examined, along with their advantages disadvantages. Artificial neural networks-based model prediction using contemporary commercial software simple develop popular. prognostic may further improved by combining marine predator algorithm, genetic swarm intelligence optimization, intelligent optimization approaches. In well-known networks fuzzy- gene-based techniques, newer ensemble such Boosted regression K-means, K-nearest neighbor (KNN), CatBoost, XGBoost gaining due architectures adaptabilities diverse types. regularly used fuzzy-based mostly black-box methods, user having little or no understanding how they function. This reason concern, ethical artificial required.

Language: Английский

Citations

245
Deep learning neural networks for short-term photovoltaic power forecasting DOI
A. Mellit, Alessandro Pavan, Vanni Lughi

et al.

Renewable Energy, Journal Year: 2021, Volume and Issue: 172, P. 276 - 288

Published: March 6, 2021

Language: Английский

Citations

241
Fundamentals and business model for resource aggregator of demand response in electricity markets DOI
Xiaoxing Lu, Kangping Li, Hanchen Xu

et al.

Energy, Journal Year: 2020, Volume and Issue: 204, P. 117885 - 117885

Published: May 21, 2020

Language: Английский

Citations

235
Comparison of machine learning methods for photovoltaic power forecasting based on numerical weather prediction DOI Creative Commons
Dávid Markovics, Martin János Mayer

Renewable and Sustainable Energy Reviews, Journal Year: 2022, Volume and Issue: 161, P. 112364 - 112364

Published: March 23, 2022

The increase of the worldwide installed photovoltaic (PV) capacity and intermittent nature solar resource highlights importance power forecasting for grid integration technology. This study compares 24 machine learning models deterministic day-ahead based on numerical weather predictions (NWP), tested two-year-long 15-min resolution datasets 16 PV plants in Hungary. effects predictor selection benefits hyperparameter tuning are also evaluated. results show that two most accurate kernel ridge regression multilayer perceptron with an up to 44.6% forecast skill score over persistence. Supplementing basic NWP data Sun position angles statistically processed irradiance values as inputs a 13.1% decrease root mean square error (RMSE), which underlines selection. is essential exploit full potential models, especially less robust prone under or overfitting without proper tuning. overall best forecasts have 13.9% lower RMSE compared baseline scenario using linear regression. Moreover, only daily average 1.5% higher than scenario, demonstrates effectiveness even limited availability. this paper can support both researchers practitioners constructing data-driven techniques NWP-based forecasting.

Language: Английский

Citations

232
Day-ahead hourly photovoltaic power forecasting using attention-based CNN-LSTM neural network embedded with multiple relevant and target variables prediction pattern DOI
Jiaqi Qu, Zheng Qian, Yan Pei

et al.

Energy, Journal Year: 2021, Volume and Issue: 232, P. 120996 - 120996

Published: May 20, 2021

Language: Английский

Citations

206
A review and taxonomy of wind and solar energy forecasting methods based on deep learning DOI Creative Commons
Ghadah Alkhayat, Rashid Mehmood

Energy and AI, Journal Year: 2021, Volume and Issue: 4, P. 100060 - 100060

Published: March 7, 2021

Renewable energy is essential for planet sustainability. output forecasting has a significant impact on making decisions related to operating and managing power systems. Accurate prediction of renewable vital ensure grid reliability permanency reduce the risk cost market Deep learning's recent success in many applications attracted researchers this field its promising potential manifested richness proposed methods increasing number publications. To facilitate further research development area, paper provides review deep learning-based solar wind published during last five years discussing extensively data datasets used reviewed works, pre-processing methods, deterministic probabilistic evaluation comparison methods. The core characteristics all works are summarised tabular forms enable methodological comparisons. current challenges future directions given. trends show that hybrid models most followed by Recurrent Neural Network including Long Short-Term Memory Gated Unit, third place Convolutional Networks. We also find multistep ahead gaining more attention. Moreover, we devise broad taxonomy using key insights gained from extensive review, believe will be understanding cutting-edge accelerating innovation field.

Language: Английский

Citations

205
A novel adaptive discrete grey model with time-varying parameters for long-term photovoltaic power generation forecasting DOI
Song Ding, Ruojin Li, Zui Tao

et al.

Energy Conversion and Management, Journal Year: 2020, Volume and Issue: 227, P. 113644 - 113644

Published: Nov. 20, 2020

Language: Английский

Citations

162
A Simplified LSTM Neural Networks for One Day-Ahead Solar Power Forecasting DOI Creative Commons
Chun‐Hung Liu, Jyh‐Cherng Gu, Ming‐Ta Yang

et al.

IEEE Access, Journal Year: 2021, Volume and Issue: 9, P. 17174 - 17195

Published: Jan. 1, 2021

In recent years, exploration and exploitation of renewable energies are turning a new chapter toward the development energy policy, technology business ecosystem in all countries. Distributed resources (DERs) being largely interconnected to electrical power grids. This dispersed intermittent generational mixes bring technical economic challenges systems terms operations, stability, reliability, interoperability policy making. additional, DERs cause significant impacts operation traditional centralized generation plants dispatch control centers. Under such circumstances, accuracy forecasting is one critical problems for TSO DSO as unit commitment, smooth fluctuations, peak load shifting, demand response, etc. this paper, simplified LSTM algorithm built over architecture Machine Learning methodology forecast day-ahead solar introduced. Through machine learning processes data processing, model fitting, cross validation, metrics evaluation hyperparameters tuning, result shows that proposed outperform MLP model. Moreover, can successfully capture intra-hour ramping on different weather scenarios. The average RMSE 0.512 which quite promising inspire best fit short-term applications.

Language: Английский

Citations

133
Automated Deep CNN-LSTM Architecture Design for Solar Irradiance Forecasting DOI
Seyed Mohammad Jafar Jalali, Sajad Ahmadian,

Abdollah Kavousi‐Fard

et al.

IEEE Transactions on Systems Man and Cybernetics Systems, Journal Year: 2021, Volume and Issue: 52(1), P. 54 - 65

Published: July 12, 2021

Accurate prediction of solar energy is an important issue for photovoltaic power plants to enable early participation in auction industries and cost-effective resource planning. This article introduces a new deep learning-based multistep ahead approach improve the forecasting performance global horizontal irradiance (GHI). A convolutional long short-term memory used extract optimal features accurate GHI. The such neural networks directly depends on their architectures. To deal with this problem, swarm evolutionary optimization method, called sine-cosine algorithm, applied advanced automatically optimize network architecture. three-phase modification model proposed increase diversity population avoid premature convergence mechanism. method investigated using three datasets collected from stations east United States. experimental results demonstrate superiority comparison other models.

Language: Английский

Citations

132