Forecasting solar power output in Ibadan: A machine learning approach leveraging weather data and system specifications

TL;DR

This paper presents a machine learning-based method to forecast hourly solar irradiance and energy output in Ibadan, Nigeria, using weather data and system specs, with Random Forest achieving the best accuracy.

Contribution

It introduces a two-stage forecasting approach combining weather data and cloud information, and applies multiple models, notably Random Forest, for improved solar energy prediction.

Findings

Random Forest achieved lowest nRMSE among models.

Seasonal models captured wet and dry season variations.

Predicted irradiance integrated with PV system data for energy output estimation.

Abstract

This study predicts hourly solar irradiance components, Global Horizontal Irradiance (GHI), Direct Normal Irradiance (DNI), and Diffuse Horizontal Irradiance (DHI) using meteorological data to forecast solar energy output in Ibadan, Nigeria. The forecasting process follows a two-stage approach: first, clear-sky irradiance values are predicted using weather variables only (e.g., temperature, humidity, wind speed); second, actual (cloudy-sky) irradiance values are forecasted by integrating the predicted clear-sky irradiance with weather variables and cloud type. Historical meteorological data were preprocessed and used to train Random Forest, Convolutional Neural Network (CNN), and Long Short-Term Memory (LSTM) models, with Random Forest demonstrating the best performance. Models were developed for annual and seasonal forecasting, capturing variations between the wet and dry seasons. The annual Random Forest model's normalised Root Mean Square Error (nRMSE) values were 0.22 for DHI, 0.33 for DNI, and 0.19 for GHI. For seasonal forecasts, wet season nRMSE values were 0.27 for DHI, 0.50 for DNI, and 0.27 for GHI, while dry season nRMSE values were 0.15 for DHI, 0.22 for DNI, and 0.12 for GHI. The predicted actual irradiance values were combined with solar system specifications (e.g., maximum power (Pmax), open-circuit voltage (Voc), short-circuit current (Isc), and AC power (Pac)) using PVLib Python to estimate the final energy output. This methodology provides a cost-effective alternative to pyranometer-based measurements, enhances grid stability for solar energy integration, and supports efficient planning for off-grid and grid-connected photovoltaic systems.