Next-Day Prediction of Hourly Solar Irradiance Using Local Weather Forecasts and LSTM Trained with Non-Local Data

Byung-Ki Jeon,Eui-Jong Kim

doi:10.3390/en13205258

Abstract

Solar irradiance prediction is significant for maximizing energy-saving effects in the predictive control of buildings. Several models for solar irradiance prediction have been developed; however, they require the collection of weather data over a long period in the predicted target region or evaluation of various weather data in real time. In this study, a long short-term memory algorithm–based model is proposed using limited input data and data from other regions. The proposed model can predict solar irradiance using next-day weather forecasts by the Korea Meteorological Administration and daily solar irradiance, and it is possible to build a model with one-time learning using national and international data. The model developed in this study showed excellent predictive performance with a coefficient of variation of the root mean square error of 12% per year even if the learning and forecast regions were different, assuming that the weather forecast was correct.

Highlights

Approximately 60% of a building’s energy is used for heating, ventilation, and air conditioning operation [1], and energy can be saved by optimally controlling the building’s heating and air conditioning systems [2]
The performance of model predictive control (MPC) control is affected by the accuracy of the hourly load prediction of a building, and the load is affected by next-day weather information; most models require weather forecast information [8,9,10]
In previous MPC studies, methods of predicting solar irradiance have been rarely reported, and most studies were conducted using the data provided by an energy analysis program or assuming that the amount of solar irradiance was completely predicted from the solar irradiance prediction model [15]

Summary

Introduction

Approximately 60% of a building’s energy is used for heating, ventilation, and air conditioning operation [1], and energy can be saved by optimally controlling the building’s heating and air conditioning systems [2]. There have been an increasing number of studies related to model predictive control (MPC), which establishes an optimal control strategy to ensure efficient air conditioner control and system operation in advance [3,4]. The performance of MPC control is affected by the accuracy of the hourly load prediction of a building, and the load is affected by next-day weather information; most models require weather forecast information [8,9,10]. Typical factors affecting the load are outdoor air temperature and solar irradiance. Prediction of outdoor air temperature is relatively easy because of small hourly changes, forecasting the actual hourly values of solar irradiance is very rare [11,12,13,14]. In previous MPC studies, methods of predicting solar irradiance have been rarely reported, and most studies were conducted using the data provided by an energy analysis program or assuming that the amount of solar irradiance was completely predicted from the solar irradiance prediction model [15]

Objectives

Results

Conclusion