A Review of Machine Learning-Based Photovoltaic Output Power Forecasting: Nordic Context

Dimd, Berhane Darsene; Völler, Steve; Cali, Ümit; Midtgård, Ole-Morten

doi:10.1109/access.2022.3156942

Cited by 31 publications

(12 citation statements)

References 88 publications

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“…On the other hand, high levels of PV penetration can cause power and voltage fluctuations due to cloud shadows, as well as an increase in energy losses when reversing power fluxes are significant. In addition, the unpredictable nature of PV power generation, which is influenced by abrupt weather changes, ultimately presents a significant challenge to integrated power infrastructures [3], [4]. Prema et al conducted an extensive review of forecast models in the context of integrating solar and wind power into main power grids.…”

Section: A Motivationmentioning

confidence: 99%

“…In the process of determining the scheduling of power generation plans and short-term dispatches, it is an essential tool for mitigating the effects of weather-induced power fluctuations. Dimd et al [4] presented a comprehensive review of machine learning (ML)-based PV output power forecasting models in the context of cold regions such as the Nordic countries and Canada. Their study focused on the impact of meteorological parameters and the effect of snow on prediction model performance, providing important insights and suggestions for model selection of ML.…”

Section: A Motivationmentioning

confidence: 99%

“…In recent years, AI and ML algorithms, such as support vector machine (SVM), and artificial neural network (ANN), have become increasingly popular for forecasting PV output power due to their ability to effectively capture the highly nonlinear relationship between environmental input parameters and PV power [11]. These models typically use both power and weather parameter measurements as inputs to the forecasting model, although limited high-performing models have been developed that only require measurements of PV power for short-term forecast horizons [4]. ANN, SVM, adaptive neuro-fuzzy (NF) networks and evolutionary optimisation have been used to forecast PV power output by using time series data of PV power and weather forecasting [12], [13].…”

Section: B Literature Reviewmentioning

confidence: 99%

See 2 more Smart Citations

Intelligent Solar Forecasts: Modern Machine Learning Models and TinyML Role for Improved Solar Energy Yield Predictions

Hayajneh,

Alasali,

Salama

et al. 2024

IEEE Access

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The advancement of sustainable energy sources necessitates the development of robust forecasting tools for efficient energy management. A prominent player in this domain, solar power, heavily relies on accurate energy yield predictions to optimize production, minimize costs, and maintain grid stability. This paper explores an innovative application of tiny machine learning to provide real-time, low-cost forecasting of solar energy yield on resource-constrained edge internet of things devices, such as micro-controllers, for improved residential and industrial energy management. To further contribute to the domain, we conduct a comprehensive evaluation of four prominent machine learning models, namely unidirectional long short-term memory, bidirectional gated recurrent unit, bidirectional long short-term memory, and simple bidirectional recurrent neural network, for predicting solar farm energy yield. Our analysis delves into the impacts of tuning the machine learning model hyperparameters on the performance of these models, offering insights to improve prediction accuracy and stability. Additionally, we elaborate on the challenges and opportunities presented by the implementation of machine learning on low-cost energy management control systems, highlighting the benefits of reduced operational expenses and enhanced grid stability. The results derived from this study offer significant implications for energy management strategies at both household and industrial scales, contributing to a more sustainable future powered by accurate and efficient solar energy forecasting.INDEX TERMS Solar power forecasting, Time series forecasting, Internet of things, Deep neural networks.

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Section: A Motivationmentioning

confidence: 99%

Section: A Motivationmentioning

confidence: 99%

Section: B Literature Reviewmentioning

confidence: 99%

See 1 more Smart Citation

Intelligent Solar Forecasts: Modern Machine Learning Models and TinyML Role for Improved Solar Energy Yield Predictions

Hayajneh,

Alasali,

Salama

et al. 2024

IEEE Access

View full text Add to dashboard Cite

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“…The deterministic component is more dominant than the stochastic component during stable weather conditions, making conventional ML algorithms such as SVM and RF viable choices. In conditions of unstable weather, in which the stochastic component is as important as the deterministic, the conventional algorithms mostly perform poorly, and DL methods are found to better capture the complex nature of the processes [95].…”

Section: Solar Power Forecastingmentioning

confidence: 99%

Artificial Intelligence for Management of Variable Renewable Energy Systems: A Review of Current Status and Future Directions

Yousef,

Rocha-Meneses

2023

Energies

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This review paper provides a summary of methods in which artificial intelligence (AI) techniques have been applied in the management of variable renewable energy (VRE) systems, and an outlook to future directions of research in the field. The VRE types included are namely solar, wind and marine varieties. AI techniques, and particularly machine learning (ML), have gained traction as a result of data explosion, and offer a method for integration of multimodal data for more accurate forecasting in energy applications. The VRE management aspects in which AI techniques have been applied include optimized power generation forecasting and integration of VRE into power grids, including the aspects of demand forecasting, energy storage, system optimization, performance monitoring, and cost management. Future directions of research in the applications of AI for VRE management are proposed and discussed, including the issue of data availability, types and quality, in addition to explainable artificial intelligence (XAI), quantum artificial intelligence (QAI), coupling AI with the emerging digital twins technology, and natural language processing.

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“…Dimd et al [19] reviewed machine learning-based PV power output forecast models in the literature in the context of the Nordic climate. Ordoñez-Palacios et al [20] predicted solar radiation in photovoltaic systems using Machine Learning techniques.…”

Section: Related Workmentioning

confidence: 99%

Supervised Machine Learning Techniques for the Prediction of the State of Charge of Batteries in Photovoltaic Systems in the Mining Sector

Pinto¹,

Tejada²,

Lopez-Casaperalta³

et al. 2022

IEEE Access

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One of the critical aspects in the mining sector is energy, being of great importance for the operation since if it were to stop, one of the consequences would be the loss of large amounts of money. The research objective is to predict the State of Charge of Batteries of equipment powered by photovoltaic solar panels in the mining sector based on automatic supervised learning techniques. A monitoring system records each energy variable programmed in the photovoltaic system, for which an analysis of the data extracted from the monitoring system was carried out. The data were evaluated using automatic supervised learning techniques using the RapidMiner tool, whose prediction average was 90.12%. The technique of automatic supervised learning of artificial neural networks was chosen to predict the state of charge of batteries for photovoltaic systems. A software tool was built with the neural network. The analysis and discussion of the results of the training of the model were carried out, the contribution of this research being to determine the prediction of the state of charge of batteries in photovoltaic systems in the mining sector using techniques of supervised machine learning which was the neural network. Finally, with the model correctly trained, validation was carried out that allowed comparing the predictive data with the data in realtime, obtaining a good relationship and satisfactory results.

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A Review of Machine Learning-Based Photovoltaic Output Power Forecasting: Nordic Context

Cited by 31 publications

References 88 publications

Intelligent Solar Forecasts: Modern Machine Learning Models and TinyML Role for Improved Solar Energy Yield Predictions

Intelligent Solar Forecasts: Modern Machine Learning Models and TinyML Role for Improved Solar Energy Yield Predictions

Artificial Intelligence for Management of Variable Renewable Energy Systems: A Review of Current Status and Future Directions

Supervised Machine Learning Techniques for the Prediction of the State of Charge of Batteries in Photovoltaic Systems in the Mining Sector

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