Dynamic Modeling and Quantum-Enhanced Forecasting of Multi-Seasonal Energy Prices in Simulated Microgrid Environments
The study address the challenge of forecasting per unit energy prices in a microgrid environment consisting of solar and hydro power resources under multi-seasonal variations. Traditional deep learning techniques such as LSTM, GRU and ESN often struggle with non-linear dependencies and volatility in...
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| Format: | Article |
| Language: | English |
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IEEE
2025-01-01
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| Series: | IEEE Access |
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| Online Access: | https://ieeexplore.ieee.org/document/11006081/ |
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| author | Ritesh Dash Anupa Sinha K. Jyotheeswara Reddy C. Dhanamjayulu Innocent Kamwa |
| author_facet | Ritesh Dash Anupa Sinha K. Jyotheeswara Reddy C. Dhanamjayulu Innocent Kamwa |
| author_sort | Ritesh Dash |
| collection | DOAJ |
| description | The study address the challenge of forecasting per unit energy prices in a microgrid environment consisting of solar and hydro power resources under multi-seasonal variations. Traditional deep learning techniques such as LSTM, GRU and ESN often struggle with non-linear dependencies and volatility in energy market. To overcome these we propose a hybrid framework incorporating Adiabatic Quantum Computing (AQC) for electricity price forecasting. The proposed AQC model encodes-32 system and market related variables into quantum states and applies adiabatic evolution to derive optimized price prediction. Simulation results using real microgrid data set-up based on HIL shows that AQC reduces forecasting error by 17.03% compared to LSTM, 14.29% to GRU and 13.88% to ESN over 24-hrs and 48-hrs horizons. The enhanced accuracy and robustness of the quantum assisted model demonstrates its potential for next generation energy market forecasting and decisions making tool. The entire framework is tested using a synthetic microgrid dataset designed to emulate real-world seasonal and operational dynamics. While this enables controlled validation of the models, the generalizability of the results to real world deployment requires further empirical evaluations on physical microgrid data set. |
| format | Article |
| id | doaj-art-b00c86f66fb74828b5bbf842450aaa6d |
| institution | DOAJ |
| issn | 2169-3536 |
| language | English |
| publishDate | 2025-01-01 |
| publisher | IEEE |
| record_format | Article |
| series | IEEE Access |
| spelling | doaj-art-b00c86f66fb74828b5bbf842450aaa6d2025-08-20T03:12:36ZengIEEEIEEE Access2169-35362025-01-0113903629038810.1109/ACCESS.2025.357071911006081Dynamic Modeling and Quantum-Enhanced Forecasting of Multi-Seasonal Energy Prices in Simulated Microgrid EnvironmentsRitesh Dash0https://orcid.org/0009-0003-2497-8126Anupa Sinha1https://orcid.org/0000-0002-0390-4406K. Jyotheeswara Reddy2https://orcid.org/0000-0002-2316-4951C. Dhanamjayulu3https://orcid.org/0009-0005-1470-9525Innocent Kamwa4https://orcid.org/0000-0002-3568-3716Computer Science Engineering, Kalinga University, Raipur, Chhattisgarh, IndiaComputer Science Engineering, Kalinga University, Raipur, Chhattisgarh, IndiaSchool of Electrical and Electronics Engineering, REVA University, Bengaluru, IndiaSchool of Electrical Engineering, Vellore Institute of Technology, Vellore, IndiaDepartment of Electrical Engineering and Computer Engineering, Laval University, Québec City, QC, CanadaThe study address the challenge of forecasting per unit energy prices in a microgrid environment consisting of solar and hydro power resources under multi-seasonal variations. Traditional deep learning techniques such as LSTM, GRU and ESN often struggle with non-linear dependencies and volatility in energy market. To overcome these we propose a hybrid framework incorporating Adiabatic Quantum Computing (AQC) for electricity price forecasting. The proposed AQC model encodes-32 system and market related variables into quantum states and applies adiabatic evolution to derive optimized price prediction. Simulation results using real microgrid data set-up based on HIL shows that AQC reduces forecasting error by 17.03% compared to LSTM, 14.29% to GRU and 13.88% to ESN over 24-hrs and 48-hrs horizons. The enhanced accuracy and robustness of the quantum assisted model demonstrates its potential for next generation energy market forecasting and decisions making tool. The entire framework is tested using a synthetic microgrid dataset designed to emulate real-world seasonal and operational dynamics. While this enables controlled validation of the models, the generalizability of the results to real world deployment requires further empirical evaluations on physical microgrid data set.https://ieeexplore.ieee.org/document/11006081/AQCLSTMGRUESNspot marketenergy trading |
| spellingShingle | Ritesh Dash Anupa Sinha K. Jyotheeswara Reddy C. Dhanamjayulu Innocent Kamwa Dynamic Modeling and Quantum-Enhanced Forecasting of Multi-Seasonal Energy Prices in Simulated Microgrid Environments IEEE Access AQC LSTM GRU ESN spot market energy trading |
| title | Dynamic Modeling and Quantum-Enhanced Forecasting of Multi-Seasonal Energy Prices in Simulated Microgrid Environments |
| title_full | Dynamic Modeling and Quantum-Enhanced Forecasting of Multi-Seasonal Energy Prices in Simulated Microgrid Environments |
| title_fullStr | Dynamic Modeling and Quantum-Enhanced Forecasting of Multi-Seasonal Energy Prices in Simulated Microgrid Environments |
| title_full_unstemmed | Dynamic Modeling and Quantum-Enhanced Forecasting of Multi-Seasonal Energy Prices in Simulated Microgrid Environments |
| title_short | Dynamic Modeling and Quantum-Enhanced Forecasting of Multi-Seasonal Energy Prices in Simulated Microgrid Environments |
| title_sort | dynamic modeling and quantum enhanced forecasting of multi seasonal energy prices in simulated microgrid environments |
| topic | AQC LSTM GRU ESN spot market energy trading |
| url | https://ieeexplore.ieee.org/document/11006081/ |
| work_keys_str_mv | AT riteshdash dynamicmodelingandquantumenhancedforecastingofmultiseasonalenergypricesinsimulatedmicrogridenvironments AT anupasinha dynamicmodelingandquantumenhancedforecastingofmultiseasonalenergypricesinsimulatedmicrogridenvironments AT kjyotheeswarareddy dynamicmodelingandquantumenhancedforecastingofmultiseasonalenergypricesinsimulatedmicrogridenvironments AT cdhanamjayulu dynamicmodelingandquantumenhancedforecastingofmultiseasonalenergypricesinsimulatedmicrogridenvironments AT innocentkamwa dynamicmodelingandquantumenhancedforecastingofmultiseasonalenergypricesinsimulatedmicrogridenvironments |