  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 development of data based PVPG forecasting. Although emerging deep
learning (DL) models, such as the long short-term memory (LSTM) model, 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) model, 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 offline LSTM model,
particularly in the presence of concept drift. Additionally, the proposed
AD-LSTM model also achieves superior performance in terms of day-ahead PVPG
forecasting compared to other traditional machine learning models and
statistical models in the literature.
