Site-specific adjustment of a NWP-based photovoltaic production forecast

Böök, Herman; Lindfors, Anders V.

doi:10.1016/j.solener.2020.10.024

Cited by 34 publications

(10 citation statements)

References 27 publications

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“…In addition to specifying the uncertainty of a forecast, information about the past performance could also be used to improve the forecast itself, for example by multiplying the forecast by 1 1+Q 2 to correct a bias if there is one. Böök and Lindfors (2020) instead suggest to adjust the forecast by "daily sets of independent adjustment coefficients C N for each hour N," which are calculated from the 90th percentiles of observed and forecasted power output in a sliding window of 30 days before the current date. This approach can, to some extent, compensate systematic errors not accounted for in the model, such as shadowing, inaccurate site metadata or systematic deviations in the environmental conditions at the site from the corresponding NWP.…”

Section: Post-processingmentioning

confidence: 99%

“…In this paper, the application of knowledge about the past forecast quality to specify the uncertainty of a forecast was discussed. Additionally, the suggestion by Böök and Lindfors (2020) represents another sensible post-processing step that could mitigate both shortcomings of the forecast method used as well as effects not accounted for in the model, such as shadowing, panel aging, soiling, or imprecise parameters.…”

Section: Concluding Observationsmentioning

confidence: 99%

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Forecast Quality of Physics-Based and Data-Driven PV Performance Models for a Small-Scale PV System

et al. 2021

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In the context of smart grids, the need for forecasts of the power output of small-scale photovoltaic (PV) arrays increases as control processes such as the management of flexibilities in the distribution grid gain importance. However, there is often only very little knowledge about the PV systems installed: even fundamental system parameters such as panel orientation, the number of panels and their type, or time series data of past PV system performance are usually unknown to the grid operator. In the past, only forecasting models that attempted to account for cause-and-effect chains existed; nowadays, also data-driven methods that attempt to recognize patterns in past behavior are available. Choosing between physics-based or data-driven forecast methods requires knowledge about the typical forecast quality as well as the requirements that each approach entails. In this contribution, the achieved forecast quality for a typical scenario (day-ahead, based on numerical weather predictions [NWP]) is evaluated for one physics-based as well as five different data-driven forecast methods for a year at the same site in south-western Germany. Namely, feed-forward neural networks (FFNN), long short-term memory (LSTM) networks, random forest, bagging and boosting are investigated. Additionally, the forecast quality of the weather forecast is analyzed for key quantities. All evaluated PV forecast methods showed comparable performance; based on concise descriptions of the forecast approaches, advantages and disadvantages of each are discussed. The approaches are viable even though the forecasts regularly differ significantly from the observed behavior; the residual analysis performed offers a qualitative insight into the achievable forecast quality in a typical real-world scenario.

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Section: Post-processingmentioning

confidence: 99%

Section: Concluding Observationsmentioning

confidence: 99%

Forecast Quality of Physics-Based and Data-Driven PV Performance Models for a Small-Scale PV System

et al. 2021

View full text Add to dashboard Cite

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“…Popular data analysis tools like principal component analysis (PCA) to construct suitable dataset for multivariate analysis of solar irradiance prediction are adopted in some earlier works [10], [11], [12]. Some earlier works show using domain knowledge based deep learning method [13], short term predictive models [14], Pearson's correlation based prediction [15], prediction with adjusting the site-specific photovoltaic (PV) output forecast [16] to predict the solar radiation.…”

Section: Introductionmentioning

confidence: 99%

Computational solar energy – Ensemble learning methods for prediction of solar power generation based on meteorological parameters in Eastern India

Chakraborty

Mondal

Barua³

et al. 2023

Renewable Energy Focus

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“…Recently, photovoltaic (PV) systems have been used intensively in distribution networks worldwide to generate electric power from sunlight beside the load centers. The total worldwide capacity of PV has experienced approximately exponential progress in the earlier decades, cumulative from 39 GWp in 2010 to 480 GWp in 2018 while the typical PV installation costs reducing from 4621 USD/kWp to 1210 USD/kWp for the same duration [ 1 ]. European Union (EU) follows an ambitious strategy to be the world leader in the sector of renewable energy by 2030 [ 2 ].…”

Section: Introductionmentioning

confidence: 99%

Promising MPPT Methods Combining Metaheuristic, Fuzzy-Logic and ANN Techniques for Grid-Connected Photovoltaic

Ali

Mahmoud

Lehtonen

et al. 2021

Sensors

122

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This paper addresses the improvement of tracking of the maximum power point upon the variations of the environmental conditions and hence improving photovoltaic efficiency. Rather than the traditional methods of maximum power point tracking, artificial intelligence is utilized to design a high-performance maximum power point tracking control system. In this paper, two artificial intelligence-based maximum power point tracking systems are proposed for grid-connected photovoltaic units. The first design is based on an optimized fuzzy logic control using genetic algorithm and particle swarm optimization for the maximum power point tracking system. In turn, the second design depends on the genetic algorithm-based artificial neural network. Each of the two artificial intelligence-based systems has its privileged response according to the solar radiation and temperature levels. Then, a novel combination of the two designs is introduced to maximize the efficiency of the maximum power point tracking system. The novelty of this paper is to employ the metaheuristic optimization technique with the well-known artificial intelligence techniques to provide a better tracking system to be used to harvest the maximum possible power from photovoltaic (PV) arrays. To affirm the efficiency of the proposed tracking systems, their simulation results are compared with some conventional tracking methods from the literature under different conditions. The findings emphasize their superiority in terms of tracking speed and output DC power, which also improve photovoltaic system efficiency.

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Site-specific adjustment of a NWP-based photovoltaic production forecast

Cited by 34 publications

References 27 publications

Forecast Quality of Physics-Based and Data-Driven PV Performance Models for a Small-Scale PV System

Forecast Quality of Physics-Based and Data-Driven PV Performance Models for a Small-Scale PV System

Computational solar energy – Ensemble learning methods for prediction of solar power generation based on meteorological parameters in Eastern India

Promising MPPT Methods Combining Metaheuristic, Fuzzy-Logic and ANN Techniques for Grid-Connected Photovoltaic

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