Transfer Learning for Renewable Energy Systems: A Survey

Al-Hajj, Rami; Assi, Ali; Neji, Bilel; Ghandour, Raymond; Barakeh, Zaher Al

doi:10.3390/su15119131

Cited by 10 publications

(4 citation statements)

References 80 publications

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“…Collecting data from multiple districts, they selected similar data based on correlation coefficients, and fine-tuned the model using target data. Al-Hajj et al in [19] report a survey of transfer learning in renewable energy systems, specifically in the prediction of solar and wind power, the prediction of load, and the diagnosis of faults. Nivarthi et al in [20] discuss the use of transfer learning in renewable energy systems, specifically in power forecasting and anomaly detection.…”

Section: Introductionmentioning

confidence: 99%

Enhanced Sequence-to-Sequence Deep Transfer Learning for Day-Ahead Electricity Load Forecasting

Laitsos,

Vontzos,

Tsiovoulos

et al. 2024

Electronics

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Electricity load forecasting is a crucial undertaking within all the deregulated markets globally. Among the research challenges on a global scale, the investigation of deep transfer learning (DTL) in the field of electricity load forecasting represents a fundamental effort that can inform artificial intelligence applications in general. In this paper, a comprehensive study is reported regarding day-ahead electricity load forecasting. For this purpose, three sequence-to-sequence (Seq2seq) deep learning (DL) models are used, namely the multilayer perceptron (MLP), the convolutional neural network (CNN) and the ensemble learning model (ELM), which consists of the weighted combination of the outputs of MLP and CNN models. Also, the study focuses on the development of different forecasting strategies based on DTL, emphasizing the way the datasets are trained and fine-tuned for higher forecasting accuracy. In order to implement the forecasting strategies using deep learning models, load datasets from three Greek islands, Rhodes, Lesvos, and Chios, are used. The main purpose is to apply DTL for day-ahead predictions (1–24 h) for each month of the year for the Chios dataset after training and fine-tuning the models using the datasets of the three islands in various combinations. Four DTL strategies are illustrated. In the first strategy (DTL Case 1), each of the three DL models is trained using only the Lesvos dataset, while fine-tuning is performed on the dataset of Chios island, in order to create day-ahead predictions for the Chios load. In the second strategy (DTL Case 2), data from both Lesvos and Rhodes concurrently are used for the DL model training period, and fine-tuning is performed on the data from Chios. The third DTL strategy (DTL Case 3) involves the training of the DL models using the Lesvos dataset, and the testing period is performed directly on the Chios dataset without fine-tuning. The fourth strategy is a multi-task deep learning (MTDL) approach, which has been extensively studied in recent years. In MTDL, the three DL models are trained simultaneously on all three datasets and the final predictions are made on the unknown part of the dataset of Chios. The results obtained demonstrate that DTL can be applied with high efficiency for day-ahead load forecasting. Specifically, DTL Case 1 and 2 outperformed MTDL in terms of load prediction accuracy. Regarding the DL models, all three exhibit very high prediction accuracy, especially in the two cases with fine-tuning. The ELM excels compared to the single models. More specifically, for conducting day-ahead predictions, it is concluded that the MLP model presents the best monthly forecasts with MAPE values of 6.24% and 6.01% for the first two cases, the CNN model presents the best monthly forecasts with MAPE values of 5.57% and 5.60%, respectively, and the ELM model achieves the best monthly forecasts with MAPE values of 5.29% and 5.31%, respectively, indicating the very high accuracy it can achieve.

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Section: Introductionmentioning

confidence: 99%

Enhanced Sequence-to-Sequence Deep Transfer Learning for Day-Ahead Electricity Load Forecasting

Laitsos,

Vontzos,

Tsiovoulos

et al. 2024

Electronics

View full text Add to dashboard Cite

show abstract

“…It collects data from multiple districts, selects similar data based on correlation coefficients, and fine-tunes the model using target data. Al-Hajj et al in [18] is a survey of transfer learning in renewable energy systems, specifically in solar and wind power forecasting, load prediction, and fault diagnosis. Nivarthi et al in [19] discuss the use of transfer learning in renewable energy systems, specifically in power forecasting and anomaly detection.…”

Section: Introductionmentioning

confidence: 99%

Enhanced Sequence-to-Sequence Deep Transfer Learning for Day-Ahead Electricity Load Forecasting

Laitsos,

Vontzos,

Tsiovoulos

et al. 2024

Preprint

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Electricity load forecasting is a crucial undertaking within all the deregulated markets globally. In contemporary times, the transition from conventional electricity grids to Smart Grids constitutes an area where extensive research is conducted on a global scale. Among the research challenges, the investigation of Deep Transfer Learning (DTL) in the field of electricity load forecasting represents a fundamental effort that imparts generality to Artificial Intelligence applications, due to new capabilities, such as knowledge transfer and computational power reduction. In this paper a comprehensive study is conducted for day-ahead electricity load forecasting. For this purpose, three Sequence-to-Sequence (Seq2seq) Deep Learning (DL) models are used, namely the Multilayer Perceptron (MLP), the Convolutional Neural Network (CNN) and the Ensemble Learning Model (ELM), which is consisted of the weighted combination of the outputs of MLP and CNN models. Also, the study focuses on the development of different forecasting strategies based on DTL and emphasizing the way the datasets are trained and fine-tuning for higher forecasting accuracy. In order to implement the forecasting strategies using Deep Learning models, load datasets from three Greek islands, Rhodes, Lesvos, and Chios, are used. The main purpose is to apply DTL for day-ahead predictions (1-24 hours) for each month of the year for Chios dataset after training and fine-tuning the models using the datasets of the three islands in various combinations. After several trials, four DTL strategies are illustrated. In the first strategy (DTL Case 1), each of the three DL models is trained using only Lesvos dataset, while fine-tuning is performed on the dataset of Chios island, in order to create day-ahead predictions for Chios load. In the second strategy (DTL Case 2), data from both Lesvos and Rhodes concurrently are used for the DL model training period, and fine-tuning is performed on the data from Chios. The third DTL strategy (DTL Case 3) contains the training of the DL models using Lesvos dataset, and the testing period performed directly on the Chios dataset without fine-tuning. The fourth strategy is a Multi-task Deep Learning (MTDL) approach, which has been extensively studied in recent years. In MTDL, the three DL models are trained simultaneously on all three datasets and the final predictions are made on the unknown part of the dataset of Chios. In this paper, we explore the performance of DTL and compare the results with those produced with MTDL. The results demonstrated that DTL can be applied with high efficiency for day-ahead load forecasting. Specifically, the two cases with fine-tuning (DTL Case 1 and 2) outperformed MTDL in terms of load prediction accuracy. Regarding the DL models, all three exhibit very high prediction accuracy, especially in the two cases with fine-tuning. The ELM excels compared to the single models. More specifically, for conducting day-ahead predictions, it has been concluded that the MLP model presents best monthly forecasts with a MAPE of 6.24% and 6.01% for the first two cases, the CNN model presents best monthly forecasts with a MAPE of 5.57% and 5.60% respectively and the ELM model achieves best monthly forecasts with a MAPE of 5.29% and 5.31%, respectively, indicating the very high accuracy it can achieve.

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“…Refs. [10][11][12] determine the electricity price in the electricity market by dynamically adjusting the electricity market for electricity markets with different degrees of flexibility. Ref.…”

Section: Introductionmentioning

confidence: 99%

Study on Dynamic Pricing Strategy for Industrial Power Users Considering Demand Response Differences in Master–Slave Game

Liu

Xing

et al. 2023

Sustainability

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With the deepening of power market reform, further study on power trading mechanisms has become the core issue of power market study. The development stage of the industrial electricity market requires efficient and flexible pricing mechanisms. Currently available pricing strategies are inadequate for demand response management. Therefore, this paper provides an in-depth study of the pricing mechanism in the industrial electricity market in the context of electricity market reform. It proposes a demand–response-based dynamic pricing strategy for industrial parks. The method proposes a dynamic pricing strategy for demand-side response in industrial parks based on master–slave game by establishing an exogenous model of demand-side response and incentives. Compared with the existing strategies, the strategy is more efficient and flexible, and effectively improves the economic efficiency of power trading and load regulation. Firstly, an exogenous model of demand-side response and incentive is built to characterize the demand-side response cost. The method focuses more on describing the exogenous characteristics of user incentives and response quantities. It only needs to analyze the exogenous indicators and random errors in various typical scenarios. The description of user demand-side response is more efficient. Secondly, a master–slave-game-based dynamic pricing strategy for industrial parks with demand-side response is proposed. The strategy is composed of a two-stage optimization. The primary regulation of customers is achieved by day-ahead time-of-use tariffs. The secondary regulation of customers is achieved by means of the same-day regulation of demand and purchase regarding clean electricity. The proposed two-stage price formation mechanism is more economical, more effective in load regulation, and improves the flexibility of industrial pricing. Finally, a case study is conducted on an industrial power user in a park in Liaoning Province. The results show that the proposed method is significantly better than existing methods in terms of improving the economic efficiency and load control effectiveness of the pricing strategy.

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Transfer Learning for Renewable Energy Systems: A Survey

Cited by 10 publications

References 80 publications

Enhanced Sequence-to-Sequence Deep Transfer Learning for Day-Ahead Electricity Load Forecasting

Enhanced Sequence-to-Sequence Deep Transfer Learning for Day-Ahead Electricity Load Forecasting

Enhanced Sequence-to-Sequence Deep Transfer Learning for Day-Ahead Electricity Load Forecasting

Study on Dynamic Pricing Strategy for Industrial Power Users Considering Demand Response Differences in Master–Slave Game

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