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Quantification of uncertainties associated with solar photovoltaic (PV) power generation forecasts is essential for optimal management of solar PV farms and their successful integration into the grid. These uncertainties can be appropriately quantified and represented in the form of probabilistic rather than deterministic. This paper introduces bootstrap confidence intervals (CIs) to quantify uncertainty...
In recent years, introduction of an alternative energy source such as solar energy is expected. However, insolation is not constant and the output of a photovoltaic (PV) system is influenced by meteorological conditions. In order to predict the power output for PV systems as accurately as possible, an insolation estimation method is required. This paper proposes the power output forecasting of a PV...
Photovoltaic (PV) systems are rapidly gaining acceptance as some of the best alternative energy sources. In installation area of large PV/battery system, it requires appropriate operation for example of a method considering power output fluctuation and insolation forecasts errors. In addition, it is better to minimize the size of battery and its capital cost. This paper proposes an optimization approach...
This paper presents a novel hybrid intelligent algorithm based on the wavelet transform (WT) and fuzzy ARTMAP (FA) network for forecasting the power output of a wind farm utilizing meteorological information such as wind speed, wind direction, and temperature. The prediction capability of the proposed hybrid WT+FA model is demonstrated by an extensive comparison with a benchmark persistence method,...
Electricity price forecasting is becoming increasingly relevant to power producers and consumers in the new competitive electric power markets, when planning bidding strategies in order to maximize their benefits and utilities, respectively. This paper proposed a method to predict hourly electricity prices for next-day electricity markets by combination methodology of ARIMA and ANN models. The proposed...
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