Enhancing PV feed-in power forecasting through federated learning with differential privacy using LSTM and GRU
Given the inherent fluctuation of photovoltaic (PV) generation, accurately forecasting solar power output and grid feed-in is crucial for optimizing grid operations. Data-driven methods facilitate efficient supply and demand management in smart grids, but predicting solar power remains challenging d...
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| Format: | Article |
| Language: | English |
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Elsevier
2024-12-01
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| Series: | Energy and AI |
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| Online Access: | http://www.sciencedirect.com/science/article/pii/S2666546824001186 |
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| author | Pascal Riedel Kaouther Belkilani Manfred Reichert Gerd Heilscher Reinhold von Schwerin |
| author_facet | Pascal Riedel Kaouther Belkilani Manfred Reichert Gerd Heilscher Reinhold von Schwerin |
| author_sort | Pascal Riedel |
| collection | DOAJ |
| description | Given the inherent fluctuation of photovoltaic (PV) generation, accurately forecasting solar power output and grid feed-in is crucial for optimizing grid operations. Data-driven methods facilitate efficient supply and demand management in smart grids, but predicting solar power remains challenging due to weather dependence and data privacy restrictions. Traditional deep learning (DL) approaches require access to centralized training data, leading to security and privacy risks. To navigate these challenges, this study utilizes federated learning (FL) to forecast feed-in power for the low-voltage grid. We propose a bottom-up, privacy-preserving prediction method using differential privacy (DP) to enhance data privacy for energy analytics on the customer side. This study aims at proving the viability of an enhanced FL approach by employing three years of meter data from three residential PV systems installed in a southern city of Germany, incorporating irradiance weather data for accurate PV power generation predictions. For the experiments, the DL models long short-term memory (LSTM) and gated recurrent unit (GRU) are federated and integrated with DP. Consequently, federated LSTM and GRU models are compared with centralized and local baseline models using rolling 5-fold cross-validation to evaluate their respective performances. By leveraging advanced FL algorithms such as FedYogi and FedAdam, we propose a method that not only predicts sequential energy data with high accuracy, achieving an R2 of 97.68%, but also adheres to stringent privacy standards, offering a scalable solution for the challenges of smart grids analytics, thus clearly showing that the proposed approach is promising and worth being pursued further. |
| format | Article |
| id | doaj-art-da65713426ff4f95accfb9fefdd053c1 |
| institution | Kabale University |
| issn | 2666-5468 |
| language | English |
| publishDate | 2024-12-01 |
| publisher | Elsevier |
| record_format | Article |
| series | Energy and AI |
| spelling | doaj-art-da65713426ff4f95accfb9fefdd053c12024-12-18T08:53:08ZengElsevierEnergy and AI2666-54682024-12-0118100452Enhancing PV feed-in power forecasting through federated learning with differential privacy using LSTM and GRUPascal Riedel0Kaouther Belkilani1Manfred Reichert2Gerd Heilscher3Reinhold von Schwerin4Institute of Databases and Information Systems, Ulm University, Ulm, 89081, Germany; Corresponding author.Smart Grids Research Group, Ulm University of Applied Sciences, Ulm, 89081, GermanyInstitute of Databases and Information Systems, Ulm University, Ulm, 89081, GermanySmart Grids Research Group, Ulm University of Applied Sciences, Ulm, 89081, GermanyInstitute of Databases and Information Systems, Ulm University, Ulm, 89081, GermanyGiven the inherent fluctuation of photovoltaic (PV) generation, accurately forecasting solar power output and grid feed-in is crucial for optimizing grid operations. Data-driven methods facilitate efficient supply and demand management in smart grids, but predicting solar power remains challenging due to weather dependence and data privacy restrictions. Traditional deep learning (DL) approaches require access to centralized training data, leading to security and privacy risks. To navigate these challenges, this study utilizes federated learning (FL) to forecast feed-in power for the low-voltage grid. We propose a bottom-up, privacy-preserving prediction method using differential privacy (DP) to enhance data privacy for energy analytics on the customer side. This study aims at proving the viability of an enhanced FL approach by employing three years of meter data from three residential PV systems installed in a southern city of Germany, incorporating irradiance weather data for accurate PV power generation predictions. For the experiments, the DL models long short-term memory (LSTM) and gated recurrent unit (GRU) are federated and integrated with DP. Consequently, federated LSTM and GRU models are compared with centralized and local baseline models using rolling 5-fold cross-validation to evaluate their respective performances. By leveraging advanced FL algorithms such as FedYogi and FedAdam, we propose a method that not only predicts sequential energy data with high accuracy, achieving an R2 of 97.68%, but also adheres to stringent privacy standards, offering a scalable solution for the challenges of smart grids analytics, thus clearly showing that the proposed approach is promising and worth being pursued further.http://www.sciencedirect.com/science/article/pii/S2666546824001186Federated learningDeep learningRecurrent neural networksData privacySolar power forecastingSmart grid |
| spellingShingle | Pascal Riedel Kaouther Belkilani Manfred Reichert Gerd Heilscher Reinhold von Schwerin Enhancing PV feed-in power forecasting through federated learning with differential privacy using LSTM and GRU Energy and AI Federated learning Deep learning Recurrent neural networks Data privacy Solar power forecasting Smart grid |
| title | Enhancing PV feed-in power forecasting through federated learning with differential privacy using LSTM and GRU |
| title_full | Enhancing PV feed-in power forecasting through federated learning with differential privacy using LSTM and GRU |
| title_fullStr | Enhancing PV feed-in power forecasting through federated learning with differential privacy using LSTM and GRU |
| title_full_unstemmed | Enhancing PV feed-in power forecasting through federated learning with differential privacy using LSTM and GRU |
| title_short | Enhancing PV feed-in power forecasting through federated learning with differential privacy using LSTM and GRU |
| title_sort | enhancing pv feed in power forecasting through federated learning with differential privacy using lstm and gru |
| topic | Federated learning Deep learning Recurrent neural networks Data privacy Solar power forecasting Smart grid |
| url | http://www.sciencedirect.com/science/article/pii/S2666546824001186 |
| work_keys_str_mv | AT pascalriedel enhancingpvfeedinpowerforecastingthroughfederatedlearningwithdifferentialprivacyusinglstmandgru AT kaoutherbelkilani enhancingpvfeedinpowerforecastingthroughfederatedlearningwithdifferentialprivacyusinglstmandgru AT manfredreichert enhancingpvfeedinpowerforecastingthroughfederatedlearningwithdifferentialprivacyusinglstmandgru AT gerdheilscher enhancingpvfeedinpowerforecastingthroughfederatedlearningwithdifferentialprivacyusinglstmandgru AT reinholdvonschwerin enhancingpvfeedinpowerforecastingthroughfederatedlearningwithdifferentialprivacyusinglstmandgru |