Deep learning based forecasting of photovoltaic power generation by incorporating domain knowledge

Luo，Xing1,2; Zhang，Dongxiao1,3; Zhu，Xu2,4

2021-06-15

10.1016/j.energy.2021.120240

ENERGY   影响因子和分区

0360-5442

Solar energy constitutes an effective supplement to traditional energy sources. However, photovoltaic power generation (PVPG) is strongly weather-dependent, and thus highly intermittent. High-precision forecasting of PVPG forms the basis of the production, transmission, and distribution of electricity, ensuring the stability and reliability of power systems. In this work, we propose a deep learning based framework for accurate PVPG forecasting. In particular, taking advantage of the long short-term memory (LSTM) network in solving sequential-data based regression problems, this paper considers the specific domain knowledge of PV and proposes a physics-constrained LSTM (PC-LSTM) to forecast the hourly day-ahead PVPG. It aims to overcome the shortcoming of recent machine learning algorithms that are applied based only on massive data, and thus easily producing unreasonable forecasts. Real-life PV datasets are adopted to evaluate the feasibility and effectiveness of the models. Sensitivity analysis is conducted for the selection of input feature variables based on a two-stage hybrid method. The results indicate that the proposed PC-LSTM model possesses stronger forecasting capability than the standard LSTM model. It is more robust against PVPG forecasting, and more suitable for PVPG forecasting with sparse data in practice. The PC-LSTM model also demonstrates superior performance with higher accuracy of PVPG forecasting compared to conventional machine learning and statistical methods.

Domain knowledge Forecasting Physics-constrained LSTM Solar energy

SCI ; EI

英语

ESI高被引

通讯

WOS:000647583700003

20211210106528

Electric power transmission ; Forecasting ; Learning algorithms ; Long short-term memory ; Photovoltaic cells ; Sensitivity analysis

Solar Energy and Phenomena:657.1 ; Electric Power Transmission:706.1.1 ; Machine Learning:723.4.2 ; Mathematics:921

ENGINEERING

2-s2.0-85102634150

Scopus

1.Intelligent Energy Lab,Peng Cheng Laboratory,Shenzhen,518055,China
2.School of Electronic and Information Engineering,Harbin Institute of Technology,Shenzhen,518055,China
3.School of Environmental Science and Engineering,Southern University of Science and Technology,Shenzhen,518055,China
4.Department of Electrical Engineering and Electronics,University of Liverpool,Liverpool,L69 3GJ,United Kingdom

Luo，Xing,Zhang，Dongxiao,Zhu，Xu. Deep learning based forecasting of photovoltaic power generation by incorporating domain knowledge[J]. ENERGY,2021,225.

Luo，Xing,Zhang，Dongxiao,&Zhu，Xu.(2021).Deep learning based forecasting of photovoltaic power generation by incorporating domain knowledge.ENERGY,225.

Luo，Xing,et al."Deep learning based forecasting of photovoltaic power generation by incorporating domain knowledge".ENERGY 225(2021).