Short-Term Solar Irradiance Forecasting Using Calibrated Probabilistic Models
Advancing probabilistic solar forecasting methods is essential to supporting the integration of solar energy into the electricity grid. In this work, we develop a variety of state-of-the-art probabilistic models for forecasting solar irradiance. We investigate the use of post-hoc calibration techniques for ensuring well-calibrated probabilistic predictions. We train and evaluate the models using public data from seven stations in the SURFRAD network, and demonstrate that the best model, NGBoost, achieves higher performance at an intra-hourly resolution than the best benchmark solar irradiance forecasting model across all stations. Further, we show that NGBoost with CRUDE post-hoc calibration achieves comparable performance to a numerical weather prediction model on hourly-resolution forecasting.
Code (0)
등록된 구현이 없습니다.
Tasks
Solar Irradiance ForecastingSimilar Papers 제목 키워드 기반
Machine learning-based probabilistic forecasting of solar irradiance in Chile
By the end of 2023, renewable sources cover 63.4% of the total electric power demand of Chile, and in line with the global trend, photovoltaic (PV) power shows the most dynamic increase. Although Chile's Atacama Desert i…
Weather ForecastingSolar Multimodal Transformer: Intraday Solar Irradiance Predictor using Public Cameras and Time Series
Accurate intraday solar irradiance forecasting is crucial for optimizing dispatch planning and electricity trading. For this purpose, we introduce a novel and effective approach that includes three distinguishing compone…
BenchmarkingSolar Irradiance ForecastingTime SeriesShort-term forecasting of global solar irradiance with incomplete data
Accurate mechanisms for forecasting solar irradiance and insolation provide important information for the planning of renewable energy and agriculture projects as well as for environmental and socio-economical studies. T…
ImputationShort-Term Solar Irradiance Forecasting Under Data Transmission Constraints
We report a data-parsimonious machine learning model for short-term forecasting of solar irradiance. The model inputs include sky camera images that are reduced to scalar features to meet data transmission constraints. T…
Solar Irradiance ForecastingConvolutional Neural Networks applied to sky images for short-term solar irradiance forecasting
Despite the advances in the field of solar energy, improvements of solar forecasting techniques, addressing the intermittent electricity production, remain essential for securing its future integration into a wider energ…
Solar Irradiance Forecasting