标题： 伊巴丹太阳能发电量预测：一种利用天气数据和系统规格的机器学习方法 显示英文标题

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

摘要： 本研究使用气象数据预测伊巴丹，尼日利亚的小时太阳辐照分量，全球水平辐照度（GHI）、直接法向辐照度（DNI）和散射水平辐照度（DHI），以预测太阳能输出。 预测过程采用两阶段方法：首先，仅使用天气变量（例如温度、湿度、风速）预测晴朗天空的辐照度值；其次，通过将预测的晴朗天空辐照度与天气变量和云类型相结合，预测实际（多云天空）辐照度值。 历史气象数据经过预处理，并用于训练随机森林、卷积神经网络（CNN）和长短期记忆（LSTM）模型，其中随机森林表现出最佳性能。 开发了年度和季节性预测模型，捕捉湿季和干季之间的变化。 年度随机森林模型的归一化均方根误差（nRMSE）值分别为DHI的0.22，DNI的0.33，GHI的0.19。 对于季节性预测，湿季的nRMSE值分别为DHI的0.27，DNI的0.50，GHI的0.27，而干季的nRMSE值分别为DHI的0.15，DNI的0.22，GHI的0.12。 预测的实际辐照度值结合太阳能系统规格（例如最大功率（Pmax）、开路电压（Voc）、短路电流（Isc）和交流功率（Pac））使用PVLib Python来估计最终的能源输出。 该方法为基于辐射计的测量提供了一种成本效益高的替代方案，增强了太阳能集成的电网稳定性，并支持离网和并网光伏系统的高效规划。 显示英文摘要

摘要： 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.