Crimson Publishers Publish With Us Reprints e-Books Video articles

Abstract

Psychology and Psychotherapy: Research Study

Medium-Term Forecasting of Solar Radiation Using Hybrid Modelling

Submission: December 06, 2022;Published: January 26, 2023

Abstract

The demand of energy is continuously increasing as a consequence of industrial growth and advancements in both developed and developing countries. Among other renewable energy resources, solar energy is one of the cleanest and abundantly available energy resources. However, it is sporadic and diurnal in nature. The accurate forecasting of solar radiation becomes essential for the effective utilization of solar energy. In this study, efforts are made to predict solar radiation on daily basis 24-hours ahead of time for the location of New Delhi (28.54 °N, 77.19 °E) which can be beneficial for day-ahead energy trading and grid operation. Statistical models such as random forest regression tree, recurrent neural network based Long Short-Term Memory Model (LSTM) and a hybrid model are used for the prediction of Global Horizontal Irradiance (GHI). The hourly dataset used for the prediction of GHI is obtained from NREL (National Renewable Energy Laboratory) from 2015 to 2020. It includes input features such as year, month, day, hour, temperature, pressure, relative humidity, wind speed and sky clearness index.
The input features are used to construct models for forecasting the global horizontal irradiance of New Delhi, India (28.54 °N, 77.19 °E). The performance of the models is evaluated by metrics such as Mean Absolute Error (MAE), Mean Square Error (MSE), Root Mean Square Error (RMSE) and coefficient of determination (R2). The results of the hybrid model are obtained as MAE of 26.22W/m2, MSE of 1304.45W/ m2, RMSE of 36.11W/m2 and R2 of 0.98 W/m2. The results indicate that the hybrid model outperforms the standalone models as it utilizes the features of both the random forest model and LSTM model to give improved R2 by 0.40% and 1.02% with respect to the random forest and LSTM model respectively. MAE is decreased by 2.42W/m2 and 5.97W/m2, MSE is decreased by 256.10W/m2 and 917.85W/m2 and RMSE is decreased by 3.38W/m2 and 11.02W/m2 with respect to the random forest and LSTM model respectively.

Keywords:Solar radiation forecasting; Global horizontal irradiance; Random Forest; Long Short-Term Memory (LSTM); Hybrid model; Coefficient of determination

Abbreviations:AR: Auto Regressive; ANN: Artificial Neural Network; ADAM: Adaptive Moment Estimation; ARMA: Autoregressive Moving Average; ARIMA: Auto Regressive Integrated Moving Average; DHI: Direct Horizontal Irradiance; DNI: Direct Normal Irradiance; GHI: Global Horizontal Irradiance; LSTM: Long Short Term Memory; ML: Machine Learning; MAE: Mean Absolute Error; MSE: Mean Square Error; RMSE: Root Mean Square Error; RELU: Rectified Linear Unit

--> Get access to the full text of this article