LSTM vs. Prophet: Achieving Superior Accuracy in Dynamic Electricity Demand Forecasting
Accurate electricity demand forecasting is critical for improving energy efficiency, maintaining grid stability, reducing operational costs, and promoting sustainability. This study presents a novel hybrid forecasting model that integrates Long Short-Term Memory (LSTM) networks and Prophet models, l...
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| Format: | Article |
| Language: | English |
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MDPI AG
2025-01-01
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| Series: | Energies |
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| Online Access: | https://www.mdpi.com/1996-1073/18/2/278 |
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| author | Saleh Albahli |
| author_facet | Saleh Albahli |
| author_sort | Saleh Albahli |
| collection | DOAJ |
| description | Accurate electricity demand forecasting is critical for improving energy efficiency, maintaining grid stability, reducing operational costs, and promoting sustainability. This study presents a novel hybrid forecasting model that integrates Long Short-Term Memory (LSTM) networks and Prophet models, leveraging their complementary strengths through a dynamic weighted ensemble methodology. The LSTM component captures nonlinear dependencies and long-term temporal patterns, while Prophet models seasonal trends and event-driven fluctuations. The hybrid model was evaluated using a comprehensive dataset of hourly electricity consumption from Ontario, Canada, achieving a Root Mean Square Error (RMSE) of 65.34, Mean Absolute Percentage Error (MAPE) of 7.3%, and an R<sup>2</sup> of 0.98. These results demonstrate significant improvements over standalone LSTM, Prophet, and other State-of-the-Art methods, highlighting the hybrid model’s adaptability and superior accuracy. This study underscores the practical implications of the hybrid approach, particularly in energy grid management and resource optimization, setting a new benchmark for time series forecasting in the energy sector. |
| format | Article |
| id | doaj-art-965c14087ab94697af58ca78756033bd |
| institution | Kabale University |
| issn | 1996-1073 |
| language | English |
| publishDate | 2025-01-01 |
| publisher | MDPI AG |
| record_format | Article |
| series | Energies |
| spelling | doaj-art-965c14087ab94697af58ca78756033bd2025-01-24T13:30:52ZengMDPI AGEnergies1996-10732025-01-0118227810.3390/en18020278LSTM vs. Prophet: Achieving Superior Accuracy in Dynamic Electricity Demand ForecastingSaleh Albahli0Department of Information Technology, College of Computer, Qassim University, Buraydah 51452, Saudi ArabiaAccurate electricity demand forecasting is critical for improving energy efficiency, maintaining grid stability, reducing operational costs, and promoting sustainability. This study presents a novel hybrid forecasting model that integrates Long Short-Term Memory (LSTM) networks and Prophet models, leveraging their complementary strengths through a dynamic weighted ensemble methodology. The LSTM component captures nonlinear dependencies and long-term temporal patterns, while Prophet models seasonal trends and event-driven fluctuations. The hybrid model was evaluated using a comprehensive dataset of hourly electricity consumption from Ontario, Canada, achieving a Root Mean Square Error (RMSE) of 65.34, Mean Absolute Percentage Error (MAPE) of 7.3%, and an R<sup>2</sup> of 0.98. These results demonstrate significant improvements over standalone LSTM, Prophet, and other State-of-the-Art methods, highlighting the hybrid model’s adaptability and superior accuracy. This study underscores the practical implications of the hybrid approach, particularly in energy grid management and resource optimization, setting a new benchmark for time series forecasting in the energy sector.https://www.mdpi.com/1996-1073/18/2/278smart environmentssmart citiesdeep learningforecastingelectricity |
| spellingShingle | Saleh Albahli LSTM vs. Prophet: Achieving Superior Accuracy in Dynamic Electricity Demand Forecasting Energies smart environments smart cities deep learning forecasting electricity |
| title | LSTM vs. Prophet: Achieving Superior Accuracy in Dynamic Electricity Demand Forecasting |
| title_full | LSTM vs. Prophet: Achieving Superior Accuracy in Dynamic Electricity Demand Forecasting |
| title_fullStr | LSTM vs. Prophet: Achieving Superior Accuracy in Dynamic Electricity Demand Forecasting |
| title_full_unstemmed | LSTM vs. Prophet: Achieving Superior Accuracy in Dynamic Electricity Demand Forecasting |
| title_short | LSTM vs. Prophet: Achieving Superior Accuracy in Dynamic Electricity Demand Forecasting |
| title_sort | lstm vs prophet achieving superior accuracy in dynamic electricity demand forecasting |
| topic | smart environments smart cities deep learning forecasting electricity |
| url | https://www.mdpi.com/1996-1073/18/2/278 |
| work_keys_str_mv | AT salehalbahli lstmvsprophetachievingsuperioraccuracyindynamicelectricitydemandforecasting |