Abstract
Ahead-of-time forecasting of the output power of power plants is essential for the stability of the electricity grid and ensuring uninterrupted service. However, forecasting renewable energy sources is difficult due to the chaotic behavior of natural energy sources. This paper presents a new approach to estimate short-term solar irradiance from sky images. The proposed algorithm extracts features from sky images and use learning-based techniques to estimate the solar irradiance. The performance of proposed machine learning (ML) algorithm is evaluated using two publicly available datasets of sky images. The datasets contain over 350,000 images for an interval of 16 years, from 2004 to 2020, with the corresponding global horizontal irradiance (GHI) of each image as the ground truth. Compared to the state-of-the-art computationally heavy algorithms proposed in the literature, our approach achieves competitive results with much less computational complexity for both nowcasting and forecasting up to 4 h ahead of time.
Highlights
Photovoltaic (PV) systems have attained a rapid increase in popularity and utilization to face the challenges of climate change and energy insecurity, as they bring a potential displacement for fossil fuel due to its merits of being pollution-free and its role of limiting global warming
The main difference between these two models is the dependency on historical data; the parametric, physical or “white box” models do not need any historical data to generate the prediction of solar irradiance
Each raw image in the dataset was down-sized into an RGB three-dimensional array. This array is directly reshaped into a 1-D input vector, which is applied to a regression model to predict solar irradiance (GHI)
Summary
Photovoltaic (PV) systems have attained a rapid increase in popularity and utilization to face the challenges of climate change and energy insecurity, as they bring a potential displacement for fossil fuel due to its merits of being pollution-free and its role of limiting global warming. Ke-Hung Lee et al [10] presented a method for short-term solar irradiance forecasting using electromagnetism-like neural networks. Ahmed et al [12] This comprehensive review included different forecasting methods, input correlation analysis, uncertainty quantification, time stamp, data pre- and post-processing, forecast horizon, network optimization, performance evaluations, weather classification and extensive reviews of ANN and other AI techniques. A major shortcoming of parametric models is the high dependency on NWP, which is spatially too coarse to accurately predict solar irradiance due to the generality of the information provided by weather forecast as well as the lack sufficient spatial and temporal resolution [5]. This study targets accurate prediction of GHI by training multiple forecasting models, using sky images obtained from the SRRL.
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