Abstract
Solar energy generated from PhotoVoltaic (PV) systems is one of the most promising types of renewable energy. However, it is highly variable as it depends on the solar irradiance and other meteorological factors. This variability creates difficulties for the large-scale integration of PV power in the electricity grid and requires accurate forecasting of the electricity generated by PV systems. In this paper we consider 2D-interval forecasts, where the goal is to predict summary statistics for the distribution of the PV power values in a future time interval. 2D-interval forecasts have been recently introduced, and they are more suitable than point forecasts for applications where the predicted variable has a high variability. We propose a method called NNE2D that combines variable selection based on mutual information and an ensemble of neural networks, to compute 2D-interval forecasts, where the two interval boundaries are expressed in terms of percentiles. NNE2D was evaluated for univariate prediction of Australian solar PV power data for two years. The results show that it is a promising method, outperforming persistence baselines and other methods used for comparison in terms of accuracy and coverage probability.
Highlights
Generating electricity from renewable sources is a key factor in the transition to a clean and sustainable energy future, to address environmental concerns and limit the global warming
In our previous work [20] we presented an application of 2D-interval forecasting for solar power prediction from previous PV power and weather data
We evaluated the performance of Network Ensemble for 2D-interval forecasting (NNE2D) for predicting the same boundaries comparison, we evaluated the performance of NNE2D for predicting the same boundaries
Summary
Generating electricity from renewable sources is a key factor in the transition to a clean and sustainable energy future, to address environmental concerns and limit the global warming. Solar energy generated from PV systems is one of the most promising and fastest growing types of renewable energy [1]. This growth is driven by government incentives that encourage the use of solar energy and by the decreasing cost of PV panels, making them more affordable. In comparison to the traditional fossil and nuclear energy sources, solar energy is freely available, can be harnessed and is environmentally free. Unlike the traditional energy sources, it is highly variable as it depends on the solar irradiance, cloud cover and other meteorological factors
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