Direct Short-Term Forecast of Photovoltaic Power through a Comparative Study between COMS and Himawari-8 Meteorological Satellite Images in a Deep Neural Network

Minho Kim,Hunsoo Song,Yongil Kim

doi:10.3390/rs12152357

Abstract

Meteorological satellite images provide crucial information on solar irradiation and weather conditions at spatial and temporal resolutions which are ideal for short-term photovoltaic (PV) power forecasts. Following the introduction of next-generation meteorological satellites, investigating their application on PV forecasts has become imminent. In this study, Communications, Oceans, and Meteorological Satellite (COMS) and Himawari-8 (H8) satellite images were inputted in a deep neural network (DNN) model for 2 hour (h)- and 1 h-ahead PV forecasts. A one-year PV power dataset acquired from two solar power test sites in Korea was used to directly forecast PV power. H8 was used as a proxy for GEO-KOMPSAT-2A (GK2A), the next-generation satellite after COMS, considering their similar resolutions, overlapping geographic coverage, and data availability. In addition, two different data sampling setups were designed to implement the input dataset. The first setup sampled chronologically ordered data using a relatively more inclusive time frame (6 a.m. to 8 p.m. in local time) to create a two-month test dataset, whereas the second setup randomly sampled 25% of data from each month from the one-year input dataset. Regardless of the setup, the DNN model generated superior forecast performance, as indicated by the lowest normalized mean absolute error (NMAE) and normalized root mean squared error (NRMSE) results in comparison to that of the support vector machine (SVM) and artificial neural network (ANN) models. The first setup results revealed that the visible (VIS) band yielded lower NMAE and NRMSE values, while COMS was found to be more influential for 1 h-ahead forecasts. For the second setup, however, the difference in NMAE results between COMS and H8 was not significant enough to distinguish a clear edge in performance. Nevertheless, this marginal difference and similarity of the results suggest that both satellite datasets can be used effectively for direct short-term PV forecasts. Ultimately, the comparative study between satellite datasets as well as spectral bands, time frames, forecast horizons, and forecast models confirms the superiority of the DNN and offers insights on the potential of transitioning to applying GK2A for future PV forecasts.

Highlights

Introduction to Meteorological Satellites for PhotovoltaicPower ForecastsSolar power or photovoltaic (PV) power generation is highly dependent on the presence of solar irradiance, which is inevitably restricted by the high variability associated with meteorological conditions and obstructing cloud coverage
This study evaluated the significance of each of the four COMS and H8 meteorological satellite image bands when integrated with time-lagged PV power data, solar geometry, and time variables in support vector machine (SVM), artificial neural network (ANN), and deep neural network (DNN) models to investigate 2 h- and 1 h-ahead PV forecast accuracy
The first setup used a relatively more inclusive time frame of “Sunlight H” and “All H” to explore the effects of the presence of solar irradiation on spectral bands for forecast accuracy, whereas the second setup only focused on time samples with solar irradiation to minimize the potential effects of seasonal bias

Summary

Introduction

Introduction to Meteorological Satellites for PhotovoltaicPower ForecastsSolar power or photovoltaic (PV) power generation is highly dependent on the presence of solar irradiance, which is inevitably restricted by the high variability associated with meteorological conditions and obstructing cloud coverage. Among the variety of remotely sensed imagery, meteorological satellites offer a potential solution to resolve these limitations by providing images at timely intervals and broad geographic coverages using multiple spectral bands. The introduction of next-generation meteorological satellites has advanced these technical specifications, including a spatial resolution of up to 500 m, data acquisition at intra-hour intervals, and a greater number of spectral bands. Short-term forecasts typically use a temporal resolution of intra-day, hourly intervals ranging from one to six hours [8,17]. To this end, the 2-hour (h) and 1 h-ahead forecast horizons used in this study can be categorized into short-term horizons which are typically important for load monitoring and forecasting [17]. For further explanation on the definitions of forecasting skill, forecast time horizons, and forecast techniques, readers are referred to the recommended references [8,9,17]

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