Abstract
Power grid operators rely on solar irradiance forecasts to manage uncertainty and variability associated with solar power. Meteorological factors such as cloud cover, wind direction, and wind speed affect irradiance and are associated with a high degree of variability and uncertainty. Statistical models fail to accurately capture the dependence between these factors and irradiance. In this paper, we introduce the idea of applying multivariate Gated Recurrent Units (GRU) to forecast Direct Normal Irradiance (DNI) hourly. The proposed GRU-based forecasting method is evaluated against traditional Long Short-Term Memory (LSTM) using historical irradiance data (i.e., weather variables that include cloud cover, wind direction, and wind speed) to forecast irradiance forecasting over intra-hour and inter-hour intervals. Our evaluation on one of the sites from Measurement and Instrumentation Data Center indicate that both GRU and LSTM improved DNI forecasting performance when evaluated under different conditions. Moreover, including wind direction and wind speed can have substantial improvement in the accuracy of DNI forecasts. Besides, the forecasting model can accurately forecast irradiance values over multiple forecasting horizons.
Highlights
Advancement in solar panel and battery technology has made solar energy generation efficient and cost-effective compared to traditional energy sources
Global Horizontal Irradiance (GHI) and Direct Normal Irradiance (DNI) are two irradiance measurements that are of interest to power grid operators, as both these measurements directly influence the performance of a solar power plant
We observe that including multivariate data improves the forecasting accuracy for both the Long Short-Term Memory (LSTM) and Gated Recurrent Units (GRU), where the multivariate model using all the variables outperform the univariate models by at least 37.43% and 36.72% for LSTM and GRU respectively
Summary
Advancement in solar panel and battery technology has made solar energy generation efficient and cost-effective compared to traditional energy sources. LSTM to forecast very short-term solar irradiance using millisecond data resolution While these models exhibit good performance, predicting longer horizons is much more difficult and important to CSP operators than a millisecond to millisecond forecast that do not provide time to plan ahead [21]. Husein and Chung studied the performance of LSTM to forecast solar irradiance based on weather information such as dry bulb temperature, dew point, humidity, wind speed, wind direction, precipitation, and cloud cover [24]. Their model outperformed traditional feedforward neural network models for all tested locations, leading to an increase in energy savings.
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