Improved multistep ahead photovoltaic power prediction model based on LSTM and self-attention with weather forecast data

• Published in: Applied energy Vol. 359; p. 122709
• Main Authors: Hu, Zehuan, Gao, Yuan, Ji, Siyu, Mae, Masayuki, Imaizumi, Taiji
• Format: Journal Article
• Language: English
• Published: Elsevier Ltd 01-04-2024
• Subjects: Long-short term memory; Long-term time series forecasting; Self-attention; Time series embeddings; Weather forecast data; Weather forecast data; Long-short term memory; Long-term time series forecasting; Self-attention; Time series embeddings
• ISSN: 0306-2619; 1872-9118
• DOI: 10.1016/j.apenergy.2024.122709

Accurate predictions of photovoltaic power generation (PV power) are essential for the integration of renewable energy into grids, markets, and building energy management systems. PV power is highly susceptible to weather conditions. Therefore, as weather forecast accuracy improves, it has become increasingly important issue to effectively utilize weather forecast data to enhance prediction accuracy. In this study, an improved model that combines Long Short-Term Memory (LSTM) and self-attention mechanisms is proposed. Proposed model captures the time features through the LSTM network and the correlations among multivariate time series through the self-attention mechanism. Additionally, methods to efficiently integrate historical and forecast data into various time-series forecasting models are also proposed. To verify the effectiveness of the proposed method and the performance of the proposed model, an actual PV power data of a building in Japan is used for various types of experiments. The results demonstrate that the proposed method effectively leverages weather forecast data and enhances the prediction performance of all models, the coefficient of determination (R2) are improved 15.8% for LSTM model, and 26.4% for proposed model. Whether for short-term or long-term predictions, proposed model consistently provides superior accuracy, practicality, and adaptability across all output sequence lengths. Compared to the basic LSTM model, R2 on short-term and long-term forecasting increased by 3.9% and 22.5%, respectively.
•A novel hybrid model using LSTM and a self-attention mechanism was proposed.•A method which can use weather forecast data for time-series forecasting models was proposed.•The proposed model and method are validated using actual data from a building in Japan.•The impact of weather forecast data on model prediction performance was studied.•Studied the effect of input and output sequence lengths on different models.