Combining transfer learning and constrained long short-term memory for power generation forecasting of newly-constructed photovoltaic plants

Photovoltaic power generation (PVPG) forecasting has attracted increasing research and industry attention due to its significance for energy management, infrastructure planning, and budgeting. Emerging deep learning (DL) models based on historical data have provided effective solutions for PVPG forecasting with great success. However, newly-constructed photovoltaic (NCPV) plants often lack col-lections of historical data, and thus it is difficult to forecast their future generation accurately. In this work, combining transfer learning (TL) and DL models, we initially propose two parameter-transferring strategies and a constrained long short-term memory (C-LSTM) model, to address the hourly day-ahead PVPG forecasting problem of NCPV plants. The K-nearest neighbors (KNN) algorithm is utilized to extract prior knowledge as physical constraints, which can guide the training process of C-LSTM. The perfor-mances of different TL methods combined with C-LSTM are evaluated specifically, and appropriate ones are determined accordingly. The proposed models are evaluated based on real-life datasets collected from actual PV plants in Australia. The results demonstrate that the proposed C-LSTM model outperforms the standard LSTM model with higher forecasting accuracy. In addition, the results also indicate that significant improvements in forecasting accuracy and stability can be obtained by the proposed TL strategies combined with C-LSTM, regardless of different sky conditions (i.e., clear sky, partly cloudy sky, and overcast sky), compared to the conventional machine learning and statistical models in the litera-ture. The forecasting skill of the combined model has improved up to 68.4% compared with the reference persistence model. (c) 2021 The Authors. Published by Elsevier Ltd. This is an open access article under the CC BY-NC-ND license (http://creativecommons.org/licenses/by-nc-nd/4.0/).

Automated summary

In this work, transfer learning and deep learning models are combined to propose strategies for parameter transferring and a constrained long short-term memory model to address the forecasting problem of newly-constructed photovoltaic plants. Evaluation based on real-life datasets from PV plants in Australia shows that the proposed models outperform traditional models with a significant improvement of up to 68.4% in forecasting accuracy.