Short-term and regionalized photovoltaic power forecasting, enhanced by reference systems, on the example of Luxembourg

Köster, Daniel; Minette, Frank; Braun, C.; O'Nagy, Oliver

doi:10.1016/j.renene.2018.08.005

Cited by 51 publications

(22 citation statements)

References 23 publications

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“…In this reference model, the forecasted value at time t + 1 is equal to the value at time t. In other words, the persistence reference model is only based on the linear correlation between the present and the future photovoltaic power values. The improvement percentage formula is given in Equation (5), where e h is the relevant error of hybrid model and e persistence is the relevant error of persistence method.…”

Section: Grey Wolf Optimization Algorithmmentioning

confidence: 99%

Daily Photovoltaic Power Prediction Enhanced by Hybrid GWO-MLP, ALO-MLP and WOA-MLP Models Using Meteorological Information

2020

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Solar energy is a safe, clean, environmentally-friendly and renewable energy source without any carbon emissions to the atmosphere. Therefore, there are many studies in the field of solar energy in order to obtain the maximum solar radiation during the day time, to estimate the amount of solar energy to be produced, and to increase the efficiency of solar energy systems. In this study, it was aimed to predict the daily photovoltaic power production using air temperature, relative humidity, total horizontal solar radiation and diffuse horizontal solar radiation parameters as multi-tupled inputs. For this purpose, grey wolf, ant lion and whale optimization algorithms were integrated to the multilayer perceptron. In addition, the effects of sigmoid, sinus and hyperbolic tangent activation functions on the prediction performance were analyzed in detail. As a result of overall accuracy indictors achieved, the grey wolf optimization algorithm-based multilayer perceptron model was found to be more successful and competitive for the daily photovoltaic power prediction. Furthermore, many meaningful patterns were revealed about the constructed models, input tuples and activation functions.

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Section: Grey Wolf Optimization Algorithmmentioning

confidence: 99%

Daily Photovoltaic Power Prediction Enhanced by Hybrid GWO-MLP, ALO-MLP and WOA-MLP Models Using Meteorological Information

2020

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“…Artificial neural networks [24,25], the SVM [26,27] and the Bayesian estimation [28] are also used in the framework of the irradiation forecasting from satellite images. Approaches also include spatio-temporal methods [29,30] and methods that combine both satellite images, NWP forecasts and ground measurements [31,32].…”

Section: Introductionmentioning

confidence: 99%

Photovoltaic Power Forecasting: Assessment of the Impact of Multiple Sources of Spatio-Temporal Data on Forecast Accuracy

2021

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The efficient integration of photovoltaic (PV) production in energy systems is conditioned by the capacity to anticipate its variability, that is, the capacity to provide accurate forecasts. From the classical forecasting methods in the state of the art dealing with a single power plant, the focus has moved in recent years to spatio-temporal approaches, where geographically dispersed data are used as input to improve forecasts of a site for the horizons up to 6 h ahead. These spatio-temporal approaches provide different performances according to the data sources available but the question of the impact of each source on the actual forecasting performance is still not evaluated. In this paper, we propose a flexible spatio-temporal model to generate PV production forecasts for horizons up to 6 h ahead and we use this model to evaluate the effect of different spatial and temporal data sources on the accuracy of the forecasts. The sources considered are measurements from neighboring PV plants, local meteorological stations, Numerical Weather Predictions, and satellite images. The evaluation of the performance is carried out using a real-world test case featuring a high number of 136 PV plants. The forecasting error has been evaluated for each data source using the Mean Absolute Error and Root Mean Square Error. The results show that neighboring PV plants help to achieve around 10% reduction in forecasting error for the first three hours, followed by satellite images which help to gain an additional 3% all over the horizons up to 6 h ahead. The NWP data show no improvement for horizons up to 6 h but is essential for greater horizons.

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“…The exploitation of renewable energy and the energy policies of this, on the other hand, have been studied more widely and in a more general way [32,33], also in respect to Luxembourg, on which some analyses have also been carried out regarding the use of photovoltaic energy [34].…”

Section: Introductionmentioning

confidence: 99%

Energy Transitions in Western European Countries: Regulation Comparative Analysis

et al. 2021

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Shifting from fossil to renewable energy sources is a major global challenge, and in this context, the European Union has promoted sustainable and environmentally friendly growth as early as the Maastricht Treaty of 1992. To date, European institutions have promulgated a series of environmental regulations and directives aimed at promoting and imposing adoption by member states of internal regulations. This paper is focused on Western Europe, and it explores, for each state under analysis, energy policies adopted, the results achieved and recommendations for the future growth of renewable energy. The results show that in countries where energy policy is not fragmented, the yield in renewable energies has been higher, and also in the states where more and various forms of subsidies are foreseen, growth seems to be greater. Finally, the paper provides useful recommendations and future policy implications for states that have not met the 2020 targets.

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Short-term and regionalized photovoltaic power forecasting, enhanced by reference systems, on the example of Luxembourg

Cited by 51 publications

References 23 publications

Daily Photovoltaic Power Prediction Enhanced by Hybrid GWO-MLP, ALO-MLP and WOA-MLP Models Using Meteorological Information

Daily Photovoltaic Power Prediction Enhanced by Hybrid GWO-MLP, ALO-MLP and WOA-MLP Models Using Meteorological Information

Photovoltaic Power Forecasting: Assessment of the Impact of Multiple Sources of Spatio-Temporal Data on Forecast Accuracy

Energy Transitions in Western European Countries: Regulation Comparative Analysis

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