Deep learning models for solar irradiance forecasting: A comprehensive review

Journal of Cleaner Production, Journal Year: 2021, Volume and Issue: 318, P. 128566 - 128566

Published: Aug. 11, 2021

Language: Английский

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A review and taxonomy of wind and solar energy forecasting methods based on deep learning

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

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State-of-the-art review on energy and load forecasting in microgrids using artificial neural networks, machine learning, and deep learning techniques

Electric Power Systems Research, Journal Year: 2023, Volume and Issue: 225, P. 109792 - 109792

Published: Sept. 8, 2023

Language: Английский

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Solar Radiation Prediction Using Different Machine Learning Algorithms and Implications for Extreme Climate Events

Frontiers in Earth Science, Journal Year: 2021, Volume and Issue: 9

Published: April 30, 2021

Solar radiation is the Earth’s primary source of energy and has an important role in surface balance, hydrological cycles, vegetation photosynthesis, weather climate extremes. The accurate prediction solar therefore very both industry research. We constructed 12 machine learning models to predict compare daily monthly values a stacking model using best these algorithms were developed radiation. results show that meteorological factors (such as sunshine duration, land temperature, visibility) are crucial models. Trend analysis between extreme temperatures amount showed importance compound events. gradient boosting regression tree (GBRT), lifting (XGBoost), Gaussian process (GPR), random forest performed better (poor) capabilities model, which included GBRT, XGBoost, GPR, models, than single but no advantage over XGBoost conclude

Language: Английский

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Predicting electrical power output of combined cycle power plants using a novel artificial neural network optimized by electrostatic discharge algorithm

Measurement, Journal Year: 2022, Volume and Issue: 198, P. 111405 - 111405

Published: May 27, 2022

Language: Английский

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Trends and gaps in photovoltaic power forecasting with machine learning

Energy Reports, Journal Year: 2022, Volume and Issue: 9, P. 447 - 471

Published: Dec. 10, 2022

The share of solar energy in the electricity mix increases year after year. Knowing production photovoltaic (PV) power at each instant time is crucial for its integration into grid. However, due to meteorological phenomena, PV output can be uncertain and continuously varying, which complicates yield prediction. In recent years, machine learning (ML) techniques have entered world forecasting help increase accuracy predictions. Researchers seen great potential this approach, creating a vast literature on topic. This paper intends identify most popular approaches gaps discipline. To do so, representative part consisting 100 publications classified based different aspects such as ML family, location systems, number systems considered, features, etc. Via classification, main trends highlighted while offering advice researchers interested

Language: Английский

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Deep learning for renewable energy forecasting: A taxonomy, and systematic literature review

Journal of Cleaner Production, Journal Year: 2022, Volume and Issue: 384, P. 135414 - 135414

Published: Dec. 15, 2022

Language: Английский

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Dual stream network with attention mechanism for photovoltaic power forecasting

Applied Energy, Journal Year: 2023, Volume and Issue: 338, P. 120916 - 120916

Published: March 20, 2023

Language: Английский

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Short-term photovoltaic power forecasting using meta-learning and numerical weather prediction independent Long Short-Term Memory models

Renewable Energy, Journal Year: 2023, Volume and Issue: 216, P. 118997 - 118997

Published: July 13, 2023

Short-term photovoltaic (PV) power forecasting is essential for integrating renewable energy sources into the grid as it provides accurate and timely information on expected output of PV systems. Deep learning (DL) networks have shown promising results in this area, but depending weather conditions particularities each system, different DL architectures may perform best. This paper proposes a meta-learning method to improve one-hour-ahead deterministic forecasts systems by dynamically blending base multiple models learn under what model performs Four long short-term memory are used produce production without using numerical predictions, with objective enhance generalizability proposed solution. The accuracy meta-learner evaluated three rooftop Lisbon, Portugal. Results indicate that best at plants, can up 5% over most per plant 4.5% equal-weighted combination forecasts. These improvements statistically significant even larger during peak hours.

Language: Английский

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Forecasting Solar Photovoltaic Power Production: A Comprehensive Review and Innovative Data-Driven Modeling Framework

Energies, Journal Year: 2024, Volume and Issue: 17(16), P. 4145 - 4145

Published: Aug. 20, 2024

The intermittent and stochastic nature of Renewable Energy Sources (RESs) necessitates accurate power production prediction for effective scheduling grid management. This paper presents a comprehensive review conducted with reference to pioneering, comprehensive, data-driven framework proposed solar Photovoltaic (PV) generation prediction. systematic integrating comprises three main phases carried out by seven modules addressing numerous practical difficulties the task: phase I handles aspects related data acquisition (module 1) manipulation 2) in preparation development scheme; II tackles associated model 3) assessment its accuracy 4), including quantification uncertainty 5); III evolves towards enhancing incorporating context change detection 6) incremental learning when new become available 7). adeptly addresses all facets PV prediction, bridging existing gaps offering solution inherent challenges. By seamlessly these elements, our approach stands as robust versatile tool precision real-world applications.

Language: Английский

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