Intelligent implementation of residential demand response using multiagent system and deep neural networks
A successful implementation of demand response (DR) always depends on proper policy and their empower technologies. This article proposed an intelligent multiagent system to idealize the residential DR in distributed network. In our model, the primary stakeholders (smart homes and retailers) are dem...
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| Veröffentlicht in: | Concurrency and computation 2021-11, Vol.33 (22), p.n/a |
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| container_title | Concurrency and computation |
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| creator | Saeed, Faisal Paul, Anand Ahmed, Muhammad Jamal Gul, Malik Junaid Jami Hong, Won‐Hwa Seo, Hyuncheol |
| description | A successful implementation of demand response (DR) always depends on proper policy and their empower technologies. This article proposed an intelligent multiagent system to idealize the residential DR in distributed network. In our model, the primary stakeholders (smart homes and retailers) are demonstrated as a multifunctional intelligent agent. Home agents (HAs) are able to predict and schedule the energy load and retailer agents (RAs) predicts wholesale market price, sells energy to HAs. Both HAs and RAs are modeled to predict the real‐time pricing. Deep neural networks, that is, long short‐term memory network and hybrid CNN‐LSTM are used to predict the electricity load and energy price. Simulation results present good accuracy. Proposed work is compared with existing model w.r.t RMSE, MSE, and MAE. Comparison shows our model outperformed the existing models. |
| doi_str_mv | 10.1002/cpe.6168 |
| format | Article |
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This article proposed an intelligent multiagent system to idealize the residential DR in distributed network. In our model, the primary stakeholders (smart homes and retailers) are demonstrated as a multifunctional intelligent agent. Home agents (HAs) are able to predict and schedule the energy load and retailer agents (RAs) predicts wholesale market price, sells energy to HAs. Both HAs and RAs are modeled to predict the real‐time pricing. Deep neural networks, that is, long short‐term memory network and hybrid CNN‐LSTM are used to predict the electricity load and energy price. Simulation results present good accuracy. Proposed work is compared with existing model w.r.t RMSE, MSE, and MAE. 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This article proposed an intelligent multiagent system to idealize the residential DR in distributed network. In our model, the primary stakeholders (smart homes and retailers) are demonstrated as a multifunctional intelligent agent. Home agents (HAs) are able to predict and schedule the energy load and retailer agents (RAs) predicts wholesale market price, sells energy to HAs. Both HAs and RAs are modeled to predict the real‐time pricing. Deep neural networks, that is, long short‐term memory network and hybrid CNN‐LSTM are used to predict the electricity load and energy price. Simulation results present good accuracy. Proposed work is compared with existing model w.r.t RMSE, MSE, and MAE. 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| subjects | Artificial neural networks CNN‐LSTM Computer networks demand response Electric power demand Electrical loads electricity Energy management Intelligent agents LSTM multiagent system Multiagent systems Neural networks Retail stores Smart buildings |
| title | Intelligent implementation of residential demand response using multiagent system and deep neural networks |
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