Post-Processing of NWP Forecasts Using Kalman Filtering With Operational Constraints for Day-Ahead Solar Power Forecasting in Thailand

Supachai Suksamosorn,Jitkomut Songsiri,Naebboon Hoonchareon

doi:10.1109/access.2021.3099481

Supachai Suksamosorn, Jitkomut Songsiri + Show 1 more

Open Access

https://doi.org/10.1109/access.2021.3099481

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Abstract

Solar power forecasting with a day-ahead horizon has played an important role in the operational planning of generating units in power system operations. We aim to develop a solar power forecasting model suitable for a tropical climate, using Thailand as a model, and hence present a linear recursive regression model as a post-processing step for reducing the errors obtained from the Weather Research and Forecasting (WRF) model. This model consists of submodels, each of which predicts the solar irradiance of a particular time of the day. By using a stepwise regression method, we found that WRF forecasts of irradiance, temperature, relative humidity, and the solar zenith angle were selected as highly relevant inputs of the model. The regression model coefficients are updated according to a Kalman filtering (KF) scheme so that the model can flexibly adapt to fluctuations in the solar irradiance. We then modify the KF update formula to accommodate the limitation in measurement availability at the time of executing the forecasts. The proposed KF formula can be generalized to find the optimal prediction given that the available measurements are mapped by an affine transformation. The obtained results using actual data from a solar rooftop system located in the central region of Thailand showed that the normalized root-mean-square error (NRMSE) of solar power prediction was about 12-13%, which was decreased from the NRMSE of the WRF model by 7-12% on average. This improvement was the best out of similar post-processing methods based on the model output statistics framework.

Highlights

R ECENT renewable energy research has focused on the techniques of solar power forecasting to enhance the power system reliability performance through a smart-grid energy management system
Spatial averaging has become a common post-processing step of numerical weather prediction (NWP) forecasts to reduce the errors [13], [29], [39]. From these points of view, we design the whole scheme of solar power forecasting in Fig. 3, where the key result of our paper is the solar irradiance forecasting module as a post-processing step from Weather Research and Forecasting (WRF) forecasts using the proposed modified Kalman filtering (KF) scheme to accommodate the operational constraints
After 10:00 h, the naive persistence model reduced the root-mean-square error (RMSE) from WRF by 11–21%, MOSlorenz was improved from the WRF by 24–39% and our method greatly reduced the RMSE by 31–42%

Summary

Introduction

R ECENT renewable energy research has focused on the techniques of solar power forecasting to enhance the power system reliability performance through a smart-grid energy management system. Recent review [2] provided an economic assessment and specified common techniques with required inputs for each forecasting temporal horizon. Several studies have concluded that NWP forecasts are more beneficial and more accurate than using cloud information from satellites for longer time horizons (15–240 h in advance) [1], [2], [5], [6]. For this reason, the widely-used methods for day-ahead forecasting have mainly included a

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