• Energy Research

The field of energy forecasting has attracted many researchers from different fields (e.g., meteorology, data sciences, mechanical or electrical engineering) over the last decade. Solar forecasting is a fast-growing sub-domain of energy forecasting. Despite several previous attempts, the methods and measures used for verification of deterministic (also known as single-valued or point) solar forecasts are still far from being standardized, making forecast analysis and comparison difficult. To analyze and compare solar forecasts, the well-established Murphy-Winkler framework for distribution-oriented forecast verification is recommended as a standard practice. This framework examines aspects of forecast quality, such as reliability, resolution, association, or discrimination, and analyzes the joint distribution of forecasts and observations, which contains all time-independent information relevant to verification. To verify forecasts, one can use any graphical display or mathematical/statistical measure to provide insights and summarize the aspects of forecast quality. The majority of graphical methods and accuracy measures known to solar forecasters are specific methods under this general framework. Additionally, measuring the overall skillfulness of forecasters is also of general interest. The use of the root mean square error (RMSE) skill score based on the optimal convex combination of climatology and persistence methods is highly recommended. By standardizing the accuracy measure and reference forecasting method, the RMSE skill score allows-with appropriate caveats-comparison of forecasts made using different models, across different locations and time periods.

The field of energy forecasting has attracted many researchers from different fields (e.g., meteorology, data sciences, mechanical or electrical engineering) over the last decade. Solar forecasting is a fast-growing sub-domain of energy forecasting. Despite several previous attempts, the methods and measures used for verification of deterministic (also known as single-valued or point) solar forecasts are still far from being standardized, making forecast analysis and comparison difficult. To analyze and compare solar forecasts, the well-established Murphy-Winkler framework for distribution-oriented forecast verification is recommended as a standard practice. This framework examines aspects of forecast quality, such as reliability, resolution, association, or discrimination, and analyzes the joint distribution of forecasts and observations, which contains all time-independent information relevant to verification. To verify forecasts, one can use any graphical display or mathematical/statistical measure to provide insights and summarize the aspects of forecast quality. The majority of graphical methods and accuracy measures known to solar forecasters are specific methods under this general framework. Additionally, measuring the overall skillfulness of forecasters is also of general interest. The use of the root mean square error (RMSE) skill score based on the optimal convex combination of climatology and persistence methods is highly recommended. By standardizing the accuracy measure and reference forecasting method, the RMSE skill score allows-with appropriate caveats-comparison of forecasts made using different models, across different locations and time periods.

Abstract The exponential growth of solar energy poses challenges to power systems, mostly due to its uncertain and variable characteristics. Hence, solar forecasting, such as very short-term solar forecasting (VSTSF), has been widely adopted to assist power system operations. The VSTSF takes inputs from various sources, among which sky image-based VSTSF is not yet well-studied compared to its counterparts. In this paper, a deep convolutional neural network (CNN) model, called the SolarNet, is developed to forecast the operational 1-h-ahead global horizontal irradiance (GHI) by only using sky images without numerical measurements and extra feature engineering. The SolarNet has a set of models that generate fixed-step GHI in parallel. Each model is composed of 20 convolutional, max-pooling, and fully-connected layers, which learns latent patterns between sky images and GHI in an end-to-end manner. Numerical results based on six years data show that the developed SolarNet outperforms the benchmarking persistence of cloudiness model and machine learning models with an 8.85% normalized root mean square error and a 25.14% forecasting skill score. The SolarNet shows superiority under various weather conditions.

Abstract The exponential growth of solar energy poses challenges to power systems, mostly due to its uncertain and variable characteristics. Hence, solar forecasting, such as very short-term solar forecasting (VSTSF), has been widely adopted to assist power system operations. The VSTSF takes inputs from various sources, among which sky image-based VSTSF is not yet well-studied compared to its counterparts. In this paper, a deep convolutional neural network (CNN) model, called the SolarNet, is developed to forecast the operational 1-h-ahead global horizontal irradiance (GHI) by only using sky images without numerical measurements and extra feature engineering. The SolarNet has a set of models that generate fixed-step GHI in parallel. Each model is composed of 20 convolutional, max-pooling, and fully-connected layers, which learns latent patterns between sky images and GHI in an end-to-end manner. Numerical results based on six years data show that the developed SolarNet outperforms the benchmarking persistence of cloudiness model and machine learning models with an 8.85% normalized root mean square error and a 25.14% forecasting skill score. The SolarNet shows superiority under various weather conditions.