Simulation-Driven Short-Term Solar Power Forecasting using LSTM Neural Networks

Abstract

The large-scale integration of photovoltaic (PV) systems into modern power grids impacts operational challenges in reducing the intermittency of solar irradiance. Short-term forecasting, especially with accurate result is essential for grid stability, economic dispatch, and demand-side management. However, the development of robust deep-learning models is frequently constrained by the limited readiness of high-quality and labeled actual generation data. This paper presents a simulation-driven methodology to address this data scarcity problem. A synthetic PV generation dataset is created using a deterministic mathematical model. This method captures the diurnal solar cycle, augmented with Gaussian stochastic noise to simulate the effects of cloud cover and atmospheric turbulence. A Long Short-Term Memory (LSTM) network in two layer is trained on the synthetic dataset and subsequently validated against real hourly PV generation data for Bali, Indonesia. This data was obtained from the PVGIS-ERA5 database. The method performs 1-hour-ahead sequence-to-point forecasting using a 6-hour retrospective window. A persistence baseline model is employed for comparative benchmarking. This experiment found that the LSTM outperforms the persistence baseline on both datasets significantly. For the empirical Bali PV dataset, the LSTM attains a Mean Absolute Error (MAE) and a Root Mean Square Error (RMSE) of 29.75 W and 43.31 W, respectively, with a corresponding R2 value of 0.9474. Compared to the persistence benchmark's MAE of 69.00 W, the network successfully cuts prediction error by 56.9%. These results validate both the simulation-driven training approach and the LSTM's capability for short-term solar forecasting under tropical conditions.