Abstract
Demand response, and artificial intelligence integration with it, have a considerable effect in optimizing energy consumption, grid stability, and promoting sustainable energy practices. Consequently, this paper presents NeuroQuMan, a comprehensive methodology for simulating demand response using a three-Qubit quantum neural network (QNN) model. NeuroQuMan integrates quantum computing and machine learning techniques to accurately predict demand based on user reaction time. The methodology encompasses an advanced structure that includes data preprocessing, three-Qubit quantum device initialization, quantum circuit definition, user decision-making, QNN predictions, loss calculations, and visualization. During the tests, NeuroQuMan achieved considerable performance values of metrics, with RMSPE of 5.41%, MAPE of 4.43%, as well as MAE of 0.37, RMSE of 0.45, and MSE of 0.21, respectively. These metrics manifest the accuracy and effectiveness of NeuroQuMan in predicting demand response. By the side of future perspectives of the work, it explores the application of advanced quantum techniques to further enhance prediction accuracy. NeuroQuMan represents the potential of quantum computing in addressing demand response challenges and provides a pathway toward more resilient and intelligent energy management systems. The findings and framework presented in this paper are utilized to advance the field of demand response and quantum-based energy management techniques using a three-Qubit structure.
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The generated dataset of the work is available for the corresponding authors upon reasonable request.
Abbreviations
- SG:
-
Smart grid
- DL:
-
Deep learning
- ML:
-
Machine learning
- QS:
-
Quantum server
- NN:
-
Neural network
- AI:
-
Artificial intelligence
- QC:
-
Quantum circuit
- DSM:
-
Demand side management
- QNN:
-
Quantum neural networks
- QVC:
-
Quantum variational circuit
- RES:
-
Renewable energy sources
- EMS:
-
Energy management system
- SEE:
-
Sustainable energy efficiency
- URT:
-
User reaction time
- SVM:
-
Support vector machine
- LSTM:
-
Long/short-term memory
- ARIMA:
-
Autoregressive integrated moving average
- SARIMA:
-
Seasonal autoregressive integrated moving average
- BiLSTM:
-
Bidirectional LSTM
- \(\psi \) :
-
Position-space wave function
- \(\varphi \) :
-
Momentum-space wave function
- \({\sigma }_{x}\) :
-
Pauli-X
- \({\sigma }_{y}\) :
-
Pauli-Y
- \({\sigma }_{z}\) :
-
Pauli-Z
- \({R}_{x}\) :
-
Rotation-X quantum gate
- \({R}_{y}\) :
-
Rotation-Y quantum gate
- \({R}_{z}\) :
-
Rotation-Z quantum gate
- \({g}_{i}\) :
-
Single quantum Qubit gates
- \({f}_{i}\) :
-
Encoding classical function
- \({D}_{i}\) :
-
Demand data
- \({\text{RG}}_{i}\) :
-
Renewable generation data
- \({\text{ED}}_{i}\) :
-
Economic dispatch
- \(\omega \) :
-
Adjustable parameters
- \(U\) :
-
Unitary quantum gate
- \(\overrightarrow{\theta }\) :
-
Parameters array
- h :
-
Plank constant
- H:
-
Hadamard
- \(t\) (s):
-
Time
- \(q\) :
-
Qubit
- \(n\) :
-
Control Qubit index
- \(m\) :
-
Target Qubit index
- \(\text{CNOT}\) :
-
Controlled-NOT
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Safari, A., Badamchizadeh, M.A. NeuroQuMan: quantum neural network-based consumer reaction time demand response predictive management. Neural Comput & Applic 36, 19121–19138 (2024). https://doi.org/10.1007/s00521-024-10201-6
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DOI: https://doi.org/10.1007/s00521-024-10201-6