An adaptive deep learning framework for day-ahead forecasting of photovoltaic power generation

Luo, Xing1; Zhang, Dongxiao1,2

2022-08-01

10.1016/j.seta.2022.102326

Sustainable Energy Technologies and Assessments   影响因子和分区

2213-1388

2213-1396

Accurate forecasts of photovoltaic power generation (PVPG) are essential to optimize operations between energy supply and demand. Recently, the propagation of sensors and smart meters has produced an enormous volume of data, which supports the data-driven models for PVPG forecasting. Although emerging deep learning (DL) models, such as the long short-term memory (LSTM), based on historical data, have provided effective solutions for PVPG forecasting with great successes, these models utilize offline learning. As a result, DL models cannot take advantage of the opportunity to learn from newly-arrived data, and are unable to handle concept drift caused by installing extra PV units and unforeseen PV unit failures. Consequently, to improve day-ahead PVPG forecasting accuracy, as well as eliminate the impacts of concept drift, this paper proposes an adaptive LSTM (AD-LSTM), which is a DL framework that can not only acquire general knowledge from historical data, but also dynamically learn specific knowledge from newly-arrived data. A two-phase adaptive learning strategy (TP-ALS) is integrated into AD-LSTM, and a sliding window (SDWIN) algorithm is proposed, to detect concept drift in PV systems. Multiple datasets from PV systems are utilized to assess the feasibility and effectiveness of the proposed approaches. The developed AD-LSTM model demonstrates greater forecasting capability than the conventional offline LSTM model, particularly in the presence of concept drift. The forecasting skill of AD-LSTM can be improved up to 73.11%. Additionally, the proposed AD-LSTM model also achieves superior performance in terms of day-ahead PVPG forecasting for individual days and multiple days to other reference models in the literature.

PV power generation forecasting Deep learning Adaptive LSTM Concept drift

SCI ; EI

英语

通讯

Science & Technology - Other Topics ; Energy & Fuels

Green & Sustainable Science & Technology ; Energy & Fuels

WOS:000836447700008

ELSEVIER

20222412211457

Economics ; Long short-term memory ; Solar cells ; Solar energy

Solar Energy and Phenomena:657.1 ; Social Sciences:971

Web of Science

1.Peng Cheng Lab, Dept Math & Theories, Shenzhen 518055, Guangdong, Peoples R China
2.Southern Univ Sci & Technol, Natl Ctr Appl Math Shenzhen NCAMS, Shenzhen 518055, Guangdong, Peoples R China

Luo, Xing,Zhang, Dongxiao. An adaptive deep learning framework for day-ahead forecasting of photovoltaic power generation[J]. Sustainable Energy Technologies and Assessments,2022,52.

Luo, Xing,&Zhang, Dongxiao.(2022).An adaptive deep learning framework for day-ahead forecasting of photovoltaic power generation.Sustainable Energy Technologies and Assessments,52.

Luo, Xing,et al."An adaptive deep learning framework for day-ahead forecasting of photovoltaic power generation".Sustainable Energy Technologies and Assessments 52(2022).