Forecasting photovoltaic production with neural networks and weather features
研究了结合天气预报数据和实体嵌入技术，使用多层感知机算法预测未来两天内光伏发电量，发现该方法比XGBoost等算法更准确。
In this paper, we address the refinement of solar energy forecasting within a 2-day window by integrating weather forecast data and strategically employing entity embedding, with a specific focus on the Multilayer Perceptron (MLP) algorithm. Through the analysis of two years of hourly solar energy production data from 16 power plants in Northern Italy (2020–2021), our research underscores the substantial impact of weather variables on solar energy production. Notably, we explore the augmentation of forecasting models by incorporating entity embedding, with a particular emphasis on embedding techniques for both general weather descriptors and individual power plants. By highlighting the nuanced integration of entity embedding within the MLP algorithm, our study reveals a significant enhancement in forecasting accuracy compared to popular machine learning algorithms like XGBoost and LGBM, showcasing the potential of this approach for more precise solar energy forecasts. • Machine Learning is effective in forecasting photovoltaic production up to 2 days ahead. • Neural networks tend to outperform standard methods. • Entity embedding enhances solar forecast accuracy. • Entity embedding reveals insights into the impact of weather categorical descriptors. • Weather data integration key for forecasting.