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3. An Evaluation of Predictor Variables for Photovoltaic Power Forecasting

Author

Visser, Lennard, AlSkaif, Tarel, van Sark, Wilfried, Filipe, Joaquim, Editorial Board Member, Ghosh, Ashish, Editorial Board Member, Prates, Raquel Oliveira, Editorial Board Member, Zhou, Lizhu, Editorial Board Member, Sanfilippo, Filippo, editor, Granmo, Ole-Christoffer, editor, Yayilgan, Sule Yildirim, editor, and Bajwa, Imran Sarwar, editor

Published
2022
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9. Solar Power Forecasts

Author

Visser, Lennard, primary, Lorenz, Elke, additional, Heinemann, Detlev, additional, and van Sark, Wilfried, additional

Published
2021
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10. Operational day-ahead solar power forecasting for aggregated PV systems with a varying spatial distribution

Author

Visser, Lennard, AlSkaif, Tarek, van Sark, Wilfried, Integration of Photovoltaic Solar Energy, Energy and Resources, Integration of Photovoltaic Solar Energy, and Energy and Resources

Subjects
Computer science, Solar power forecasting, PV aggregation, Day-ahead markets, Solar forecast, Machine learning, Econometrics, Production (economics), Renewable Energy, Solar power, Sustainability and the Environment, Renewable Energy, Sustainability and the Environment, business.industry, Deep learning, Photovoltaic system, Toegepaste Informatiekunde, Grid, Industrial engineering, Ensemble learning, Photovoltaics, Support vector machine, Regional solar forecasting, Memory model, Artificial intelligence, Information Technology, LSTM, business
Abstract

Accurate forecasts of the power production of distributed photovoltaic (PV) systems are essential to support grid operation and enable a high PV penetration rate in the electricity grid. In this study, we analyse the performance of 12 different models that forecast the day-ahead power production in agreement with market conditions. These models include regression, support vector regression, ensemble learning, deep learning and physical based techniques. In addition, we examine the effect of aggregating multiple PV systems with a varying inter-system distance on the forecast model performance. The models are evaluated both on their technical and economic performance. From a technical perspective, the results show a positive effect from both an increasing inter-system distance and a larger sized PV fleet on the model performance, which was not the case for the economic assessment. Furthermore, the ensemble and deep learning models perform better than the alternatives from a technical point of view. For the economic assessment, the results indicate the superiority of the physical based model, followed by the deep learning models. Lastly, our findings show the importance of considering the user's objective when assessing solar power forecast models.

Published
2022

12. Solar Irradiance Ramp Forecasting Based on All-Sky Imagers

Author

Logothetis, Stavros-Andreas, primary, Salamalikis, Vasileios, additional, Nouri, Bijan, additional, Remund, Jan, additional, Zarzalejo, Luis F., additional, Xie, Yu, additional, Wilbert, Stefan, additional, Ntavelis, Evangelos, additional, Nou, Julien, additional, Hendrikx, Niels, additional, Visser, Lennard, additional, Sengupta, Manajit, additional, Pó, Mário, additional, Chauvin, Remi, additional, Grieu, Stephane, additional, Blum, Niklas, additional, Sark, Wilfried van, additional, and Kazantzidis, Andreas, additional

Published
2022
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14. Solar irradiance ramp events forecasting based on all-sky imagers

Author

Logothetis, Stavros-Andreas, Salamalikis, Vasilios, Wilbert, Stefan, Remund, Jan, Zarzalejo, L. F., Xie, Yu, Nouri, Bijan, Ntavelis, Evangelos, Nou, Julien, Hendrikx, Niels, Visser, Lennard, Sengupta, Manajit, Pó, Mário, Chauvin, Remi, Grieu, Stephane, Blum, Niklas, van Sark, Willfried, and Kazantzidis, A.

Subjects
irradiance, forecasting, ramps, all sky imager
Published
2022

16. An evaluation of predictor variables for photovoltaic power forecasting

Author

Visser, Lennard, AlSkaif, Tarek, van Sark, Wilfried, Integration of Photovoltaic Solar Energy, and Energy and Resources

Subjects
Photovoltaics, weather forecasts, machine learning, solar power forecasting, predictor variables
Abstract

Accurate forecasts of the electric power generation by solar Photovoltaic (PV) systems are essential to support their vast increasing integration. This study evaluates the interdependence of 14 predictor variables and their importance to machine learning (ML) models that forecast the day-ahead PV power production. To this purpose, we use two feature selection models to rank the predictor variables and accordingly, examine the performance change of two ML forecast models when a growing number of variables is considered. The study is performed using 3 years of data for Utrecht, the Netherlands. The results show the most important variables for PV power forecasting and identifies how many top variables should be considered to achieve an optimal forecast performance accuracy. Additionally, the best forecast model performance is found when only a few predictor variables are considered, including a created variable that estimates the PV power output based on technical system characteristics and physical relations.

Published
2021

17. Forecasting of solar irradiance and ramp events with all-sky imagers

Author

Logothetis, Stavros-Andreas, primary, Salamalikis, Vasileios, additional, Wilbert, Stefan, additional, Remund, Jan, additional, Zarzalejo, Luis, additional, Xie, Yu, additional, Nouri, Bijan, additional, Ntavelis, Evangelos, additional, Nou, Julien, additional, Visser, Lennard, additional, Sengupta, Manajit, additional, Pó, Mário, additional, Chauvin, Remi, additional, Grieu, Stephane, additional, Sark, Wilfried Van, additional, and Kazantzidis, Andreas, additional

Published
2021
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19. Regional solar power forecasting

Author

Betti, Alessandro, Pierro, Marco, Cornaro, Cristina, Moser, David, Moschella, Michela, Collino, Elena, Ronzio, Dario, Meer, Dennis Van Der, Widén, Joakim, Visser, Lennard, T. AlSkaif, Sark, Wilfried Van, and Remund, Jan

Published
2020
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22. Erratum: "Open-source quality control routine and multi-year power generation data of 175 PV systems" [J. Renewable Sustainable Energy 14, 043501 (2022)].

Author

Visser, Lennard R., Elsinga, Boudewijn, AlSkaif, Tarek A., and van Sark, Wilfried G. J. H. M.

Subjects
*CLEAN energy, *PHOTOVOLTAIC power systems, *RENEWABLE energy sources, *QUALITY control
Abstract

This document is an erratum for an article titled "Open-source quality control routine and multi-year power generation data of 175 PV systems." The authors acknowledge that there were minor typos in equations (7)-(10) of the original article. They provide the corrected versions of the equations and state that the explanation, results, and code used in the original work were correct, so the results and conclusions remain unchanged. The corrected equations involve the measured power output of a PV system, inverter capacity, a constant value, and other variables. The authors also mention the simulated power output for clear sky conditions and provide additional details about threshold values and the DC capacity of the PV system. [Extracted from the article]

Published
2024
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