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Forecasting of Photovoltaic Power Generation and Model Optimization
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Forecasting of Photovoltaic Power Generation and Model Optimization

A special issue of Energies (ISSN 1996-1073). This special issue belongs to the section "A2: Solar Energy and Photovoltaic Systems".

Deadline for manuscript submissions: 8 May 2025 | Viewed by 5419

Special Issue Editors

Prof. Dr. Filippo Spertino

E-Mail Website
Guest Editor
Department of Energy, Politecnico di Torino, 10129 Torino, Italy
Interests: photovoltaic and wind power systems
Special Issues, Collections and Topics in MDPI journals
Prof. Dr. Paolo Di Leo

E-Mail Website1 Website2
Guest Editor
Department of Energy, Politecnico di Torino, 10129 Torino, Italy
Interests: renewable energy technologies; electrical power engineering; power systems analysis
Special Issues, Collections and Topics in MDPI journals

Special Issue Information

Dear Colleagues,

We are pleased to announce a Special Issue focused on "Forecasting of Photovoltaic Power Generation and Model Optimization." In today's rapidly evolving energy landscape, photovoltaic (PV) power generation has emerged as a key player in renewable energy sources. The widespread adoption of PV systems, ranging from small-scale residential installations to large solar farms, necessitates accurate forecasting techniques to ensure optimal integration and utilization in the power grid.

This Special Issue aims to showcase the latest advancements in PV power generation forecasting methodologies and model optimization techniques. As PV systems are influenced by various factors such as weather conditions, solar irradiance, temperature, and other dynamic variables, the development of robust forecasting models becomes paramount. Additionally, with an ever-increasing focus on efficiency and sustainability, optimizing PV system models has become crucial for enhancing performance and maximizing energy output.

Topics of interest for publication include, but are not limited to:

  • Novel forecasting approaches for PV power generation;
  • Data-driven forecasting techniques;
  • Machine learning and artificial intelligence for PV forecasting;
  • Hybrid forecasting models combining statistical and machine learning methods;
  • Forecasting uncertainty quantification and risk assessment;
  • Spatial and temporal forecasting of PV generation;
  • Integration of weather data and climate models in forecasting;
  • Model optimization for improving PV system efficiency;
  • Real-time forecasting and control strategies;
  • Integration of energy storage for enhanced PV power dispatch;
  • Forecasting for PV microgrid and off-grid systems;
  • Forecasting applications in energy trading and market operations;
  • Case studies and practical implementations of forecasting and model optimization.

We invite researchers, engineers, and practitioners to contribute their original research articles, review papers, and technical notes to this Special Issue. By bringing together diverse perspectives and cutting-edge research, we aim to foster innovation and collaboration in the domain of PV power generation forecasting and model optimization. We believe that this collection of research will significantly advance the field, facilitating the seamless integration of photovoltaic systems into the global energy landscape.

We look forward to your valuable contributions to this Special Issue.

Prof. Dr. Filippo Spertino
Prof. Dr. Paolo Di Leo
Guest Editors

Manuscript Submission Information

Manuscripts should be submitted online at www.mdpi.com by registering and logging in to this website. Once you are registered, click here to go to the submission form. Manuscripts can be submitted until the deadline. All submissions that pass pre-check are peer-reviewed. Accepted papers will be published continuously in the journal (as soon as accepted) and will be listed together on the special issue website. Research articles, review articles as well as short communications are invited. For planned papers, a title and short abstract (about 100 words) can be sent to the Editorial Office for announcement on this website.

Submitted manuscripts should not have been published previously, nor be under consideration for publication elsewhere (except conference proceedings papers). All manuscripts are thoroughly refereed through a single-blind peer-review process. A guide for authors and other relevant information for submission of manuscripts is available on the Instructions for Authors page. Energies is an international peer-reviewed open access semimonthly journal published by MDPI.

Please visit the Instructions for Authors page before submitting a manuscript. The Article Processing Charge (APC) for publication in this open access journal is 2600 CHF (Swiss Francs). Submitted papers should be well formatted and use good English. Authors may use MDPI's English editing service prior to publication or during author revisions.

Keywords

  • photovoltaic power generation
  • renewable energy
  • solar energy forecasting
  • model optimization
  • predictive modeling
  • weather data
  • machine learning
  • data-driven models
  • uncertainty quantification
  • energy optimization
  • weather modeling
  • forecasting accuracy

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Published Papers (5 papers)

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Research

14 pages, 17020 KiB  
Article
A Long Short-Term Memory–Wasserstein Generative Adversarial Network-Based Data Imputation Method for Photovoltaic Power Output Prediction
by Zhu Liu, Lingfeng Xuan, Dehuang Gong, Xinlin Xie and Dongguo Zhou
Energies 2025, 18(2), 399; https://doi.org/10.3390/en18020399 - 17 Jan 2025
Viewed by 380
Abstract
To address the challenges of the issue of inaccurate prediction results due to missing data in PV power records, a photovoltaic power data imputation method based on a Wasserstein Generative Adversarial Network (WGAN) and Long Short-Term Memory (LSTM) network is proposed. This method [...] Read more.
To address the challenges of the issue of inaccurate prediction results due to missing data in PV power records, a photovoltaic power data imputation method based on a Wasserstein Generative Adversarial Network (WGAN) and Long Short-Term Memory (LSTM) network is proposed. This method introduces a data-driven GAN framework with quasi-convex characteristics to ensure the smoothness of the imputed data with the existing data and employs a gradient penalty mechanism and a single-batch multi-iteration strategy for stable training. Finally, through frequency domain analysis, t-Distributed Stochastic Neighbor Embedding (t-SNE) metrics, and prediction performance validation of the generated data, the proposed method can improve the continuity and reliability of data in photovoltaic prediction tasks. Full article
(This article belongs to the Special Issue Forecasting of Photovoltaic Power Generation and Model Optimization)
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16 pages, 6812 KiB  
Article
Predicting Photovoltaic Module Lifespan Based on Combined Stress Tests and Latent Heat Analysis
by Woojun Nam, Jinho Choi, Gyugwang Kim, Jinhee Hyun, Hyungkeun Ahn and Neungsoo Park
Energies 2025, 18(2), 304; https://doi.org/10.3390/en18020304 - 11 Jan 2025
Viewed by 796
Abstract
In this study, long-term reliability tests for high-power-density photovoltaic (PV) modules were introduced and analyzed in accordance with IEC 61215 and light-combined damp heat cycles, such as DIN 75220. The results indicated that post light soaking procedure, light-combined damp heat cycles caused a [...] Read more.
In this study, long-term reliability tests for high-power-density photovoltaic (PV) modules were introduced and analyzed in accordance with IEC 61215 and light-combined damp heat cycles, such as DIN 75220. The results indicated that post light soaking procedure, light-combined damp heat cycles caused a 3.51% power drop, while IEC standard tests (DH1000 and TC200) caused only 0.87% and 1.32% power drops, respectively. IEC 61215 failed to assess the long-term reliability of the high-power-density PV module, such as the passivated emitter rear cell. Additionally, based on the combined test, the latent heat (Qmod) of the module was introduced to predict its degradation rate and to fit the prediction curve of the product guaranteed by the PV module manufacturers. Qmod facilitates in predicting a PV module’s lifespan according to the environmental factors of the actual installation area. The Qmod values of the PV stations in water environments, such as floating and/or marine PVs, indicated that they would last 7.2 years more than those on a rooftop, assuming that latent heat is the only cause of deterioration. Therefore, extending module life and improving power generation efficiency by determining installation sites to minimize latent heat would be advantageous. Full article
(This article belongs to the Special Issue Forecasting of Photovoltaic Power Generation and Model Optimization)
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25 pages, 5030 KiB  
Article
Global Horizontal Irradiance in Brazil: A Comparative Study of Reanalysis Datasets with Ground-Based Data
by Margarete Afonso de Sousa Guilhon Araujo, Soraida Aguilar, Reinaldo Castro Souza and Fernando Luiz Cyrino Oliveira
Energies 2024, 17(20), 5063; https://doi.org/10.3390/en17205063 - 11 Oct 2024
Viewed by 882
Abstract
Renewable energy sources are increasing globally, mainly due to efforts to achieve net zero emissions. In Brazil, solar photovoltaic electricity generation has grown substantially in recent years, with the installed capacity rising from 2455 MW in 2018 to 47,033 MW in August 2024. [...] Read more.
Renewable energy sources are increasing globally, mainly due to efforts to achieve net zero emissions. In Brazil, solar photovoltaic electricity generation has grown substantially in recent years, with the installed capacity rising from 2455 MW in 2018 to 47,033 MW in August 2024. However, the intermittency of solar energy increases the challenges of forecasting solar generation, making it more difficult for decision-makers to plan flexible and efficient distribution systems. In addition, to forecast power generation to support grid expansion, it is essential to have adequate data sources, but measured climate data in Brazil is limited and does not cover the entire country. To address this problem, this study evaluates the global horizontal irradiance (GHI) of four global reanalysis datasets—MERRA-2, ERA5, ERA5-Land, and CFSv2—at 35 locations across Brazil. The GHI time series from reanalysis was compared with ground-based measurements to assess its ability to represent hourly GHI in Brazil. Results indicate that MERRA-2 performed best in 90% of the locations studied, considering the root mean squared error. These findings will help advance solar forecasting by offering an alternative in regions with limited observational time series measurements through the use of reanalysis datasets. Full article
(This article belongs to the Special Issue Forecasting of Photovoltaic Power Generation and Model Optimization)
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16 pages, 3263 KiB  
Article
Informer Short-Term PV Power Prediction Based on Sparrow Search Algorithm Optimised Variational Mode Decomposition
by Wu Xu, Dongyang Li, Wenjing Dai and Qingchang Wu
Energies 2024, 17(12), 2984; https://doi.org/10.3390/en17122984 - 17 Jun 2024
Cited by 2 | Viewed by 1015
Abstract
The output power of PV systems is influenced by various factors, resulting in strong volatility and randomness, which makes it difficult to forecast. Therefore, this paper proposes an Informer prediction model based on optimised VMD for predicting short-term PV power. Firstly, the temporal [...] Read more.
The output power of PV systems is influenced by various factors, resulting in strong volatility and randomness, which makes it difficult to forecast. Therefore, this paper proposes an Informer prediction model based on optimised VMD for predicting short-term PV power. Firstly, the temporal coding of the Informer model is improved and, secondly, the original sequence is decomposed into multiple modal components using VMD, and then optimisation of the results of VMD in conjunction with the optimisation strategy of SSA improves the characteristics of the time series data. Finally, the refined data are fed into the Informer framework for modelling and prediction, utilising the self-attention mechanism and multiscale feature fusion of Informer to precisely forecast PV power. The power of PV prediction data from the SSA-VMD-Informer model and four other commonly used models is compared. Experimental results indicate that the SSA-VMD-Informer model performs exceptionally well in short-term PV power prediction, achieving higher accuracy than traditional methods. As an example, the results of predicting the PV power on 24 April in a region of Xinjiang are 1.3882 for RMSE, 0.8310 for MSE, 1.14 for SDE, and 0.9944 for R2. Full article
(This article belongs to the Special Issue Forecasting of Photovoltaic Power Generation and Model Optimization)
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33 pages, 7250 KiB  
Article
Forecasting Solar Energy Generation and Household Energy Usage for Efficient Utilisation
by Aistis Raudys and Julius Gaidukevičius
Energies 2024, 17(5), 1256; https://doi.org/10.3390/en17051256 - 6 Mar 2024
Cited by 1 | Viewed by 1555
Abstract
In this study, a prototype was developed for the effective utilisation of a domestic solar power plant. The basic idea is to switch on certain electrical appliances when the surplus of generated energy is predicted one hour in advance, for example, switching on [...] Read more.
In this study, a prototype was developed for the effective utilisation of a domestic solar power plant. The basic idea is to switch on certain electrical appliances when the surplus of generated energy is predicted one hour in advance, for example, switching on a pump motor for watering a garden. This prediction is important because some devices (motors) wear out if they are switched on and off too frequently. If a solar power plant generates more energy than a household can consume, the surplus energy is fed into the main grid for storage. If a household has an energy shortage, the same energy is bought back at a higher price. In this study, data were collected from solar inverters, historical weather APIs and smart energy meters. This study describes the data preparation process and feature engineering that will later be used to create forecasting models. This study consists of two forecasting models: solar energy generation and household electricity consumption. Both types of model were tested using Facebook Prophet and different neural network architectures: feedforward, long short-term memory (LSTM) and gated recurrent unit (GRU) networks. In addition, a baseline model was developed to compare the prediction accuracy. Full article
(This article belongs to the Special Issue Forecasting of Photovoltaic Power Generation and Model Optimization)
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