Probabilistic Forecasting Model of Solar Power Outputs Based on the Naïve Bayes Classifier and Kriging Models

Seungbeom Nam,Jin Hur

doi:10.3390/en11112982

Abstract

Solar power’s variability makes managing power system planning and operation difficult. Facilitating a high level of integration of solar power resources into a grid requires maintaining the fundamental power system so that it is stable when interconnected. Accurate and reliable forecasting helps to maintain the system safely given large-scale solar power resources; this paper therefore proposes a probabilistic forecasting approach to solar resources using the R statistics program, applying a hybrid model that considers spatio-temporal peculiarities. Information on how the weather varies at sites of interest is often unavailable, so we use a spatial modeling procedure called kriging to estimate precise data at the solar power plants. The kriging method implements interpolation with geographical property data. In this paper, we perform day-ahead forecasts of solar power based on the probability in one-hour intervals by using a Naïve Bayes Classifier model, which is a classification algorithm. We augment forecasting by taking into account the overall data distribution and applying the Gaussian probability distribution. To validate the proposed hybrid forecasting model, we perform a comparison of the proposed model with a persistence model using the normalized mean absolute error (NMAE). Furthermore, we use empirical data from South Korea’s meteorological towers (MET) to interpolate weather variables at points of interest.

Highlights

Solar energy utilization is rapidly growing all over the world
The results demonstrated that the Numerical Weather Prediction (NWP) exogenous inputs improve the quality of the intra-day probabilistic forecasts
The results have shown that the ensemble models offer even more accurate results than any individual machine learning model like autoregressive integrated moving average (ARIMA)

Summary

Introduction

Solar energy utilization is rapidly growing all over the world. According to the International. Canadas Global Environmental Multiscale NWP model to forecast hourly Global Horizontal Irradiance (GHI) and solar power for horizons out to 48 h They applied spatial averaging and bias removal using a Kalman filter on the NWP forecasts to increase the predictions’ accuracy. In AI forecasting, reference [12] used an ANN with exogenous variables to forecast the hourly solar power for a forecasting horizon of 12 h This model shows an improvement in root-mean-square error (RMSE) of about 2.07%. Reference [21] shows three different methods for ensemble probabilistic forecasting, derived from seven individual machine learning models, to generate 24-h-ahead solar power forecasts. We apply a hybrid spatio-temporal forecasting model that combines kriging and naïve Bayes Classifier based on empirical NWP data in South Korea. We perform a comparison of the proposed model with a persistence model using normalized mean absolute error (NMAE) to validate the proposed hybrid forecasting model

A Hybrid

Spatial

Probabilistic

Estimating Weather Data at Solar Farm “A” Using the Kriging Technique

Findings

Conclusions