Accurate one step and multistep forecasting of very short-term PV power using LSTM-TCN model

计算机科学 调度(生产过程) 气象学 期限(时间) 时间序列 人工智能 机器学习 数学 数学优化 地理 物理 量子力学
作者
Tariq Limouni,Reda Yaagoubi,K. Bouziane,Khalid Guissi,El Houssain Baali
出处
期刊:Renewable Energy [Elsevier BV]
卷期号:205: 1010-1024 被引量:170
标识
DOI:10.1016/j.renene.2023.01.118
摘要

Accurate PV power forecasting is becoming a mandatory task to integrate the PV plant into the electrical grid, scheduling and guaranteeing the safety of the power grid. In this paper, a novel model to forecast the PV power using LSTM-TCN has been proposed. It consists of a combination between Long Short Term Memory and Temporal Convolutional Network models. LSTM is used to extract the temporal features from input data, then combined with TCN to build the connection between features and outputs. The proposed model has been tested using a dataset that includes historical time series of measured PV power. The accuracy of this model is then compared to LSTM and TCN models in different seasons, time periods forecast, cloudy, clear, and intermittent days. For one step forecasting, the results show that our proposed model outperforms the LSTM and TCN model. It has carried out a reduction of 8.47%, 14.26% for the autumn season, 6.91%,15.18 for the winter season, 10.22%,14.26% for spring season and 14.26%, 14.23% for the summer season on the Mean Absolute Error compared with LSTM, TCN. For multistep forecasting, LSTM-TCN surpassed all compared models in different time periods forecast from 2 steps to 7 steps PV power forecasting.
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