Potential of Model-Free Control for Demand-Side Management Considering Real-Time Pricing
This paper presents a detailed description of data predictive control (DPC) applied to a demand-side energy management system. Different from traditional model-based predictive control (MPC) algorithms, this approach introduces two model-free algorithms of artificial neural network (ANN) and random...
| Main Authors: | , , , |
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| Format: | Article |
| Language: | English |
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MDPI AG
2019-07-01
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| Series: | Energies |
| Subjects: | |
| Online Access: | https://www.mdpi.com/1996-1073/12/13/2587 |
| _version_ | 1798034574126612480 |
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| author | Suyang Zhou Fenghua Zou Zhi Wu Wei Gu |
| author_facet | Suyang Zhou Fenghua Zou Zhi Wu Wei Gu |
| author_sort | Suyang Zhou |
| collection | DOAJ |
| description | This paper presents a detailed description of data predictive control (DPC) applied to a demand-side energy management system. Different from traditional model-based predictive control (MPC) algorithms, this approach introduces two model-free algorithms of artificial neural network (ANN) and random forest (RF) to make control strategy predictions on system operation, while avoiding the huge cost and effort associated with learning a grey/white box model of the physical system. The operating characteristics of electrical appliances, system energy consumption, and users’ comfort zones are also considered in the selected energy management system based on a real-time electricity pricing system. Case studies consisting of two scenarios (0% and 15% electricity price fluctuation) are delivered to demonstrate the effectiveness of the proposed approach. Simulation results demonstrate that the DPC controller based on ANN pays only 0.18% additional bill cost to maintain users’ comfort zones and system economy standardization while using only 0.096% optimization time cost compared with the MPC controller. |
| first_indexed | 2024-04-11T20:46:07Z |
| format | Article |
| id | doaj.art-435a5a5ce0694d76b5f0054a4952f0e8 |
| institution | Directory Open Access Journal |
| issn | 1996-1073 |
| language | English |
| last_indexed | 2024-04-11T20:46:07Z |
| publishDate | 2019-07-01 |
| publisher | MDPI AG |
| record_format | Article |
| series | Energies |
| spelling | doaj.art-435a5a5ce0694d76b5f0054a4952f0e82022-12-22T04:04:01ZengMDPI AGEnergies1996-10732019-07-011213258710.3390/en12132587en12132587Potential of Model-Free Control for Demand-Side Management Considering Real-Time PricingSuyang Zhou0Fenghua Zou1Zhi Wu2Wei Gu3School of Electrical Engineering, Southeast University, 2 Sipailou Xuanwu Qu, Nanjing 210096, ChinaSchool of Electrical Engineering, Southeast University, 2 Sipailou Xuanwu Qu, Nanjing 210096, ChinaSchool of Electrical Engineering, Southeast University, 2 Sipailou Xuanwu Qu, Nanjing 210096, ChinaSchool of Electrical Engineering, Southeast University, 2 Sipailou Xuanwu Qu, Nanjing 210096, ChinaThis paper presents a detailed description of data predictive control (DPC) applied to a demand-side energy management system. Different from traditional model-based predictive control (MPC) algorithms, this approach introduces two model-free algorithms of artificial neural network (ANN) and random forest (RF) to make control strategy predictions on system operation, while avoiding the huge cost and effort associated with learning a grey/white box model of the physical system. The operating characteristics of electrical appliances, system energy consumption, and users’ comfort zones are also considered in the selected energy management system based on a real-time electricity pricing system. Case studies consisting of two scenarios (0% and 15% electricity price fluctuation) are delivered to demonstrate the effectiveness of the proposed approach. Simulation results demonstrate that the DPC controller based on ANN pays only 0.18% additional bill cost to maintain users’ comfort zones and system economy standardization while using only 0.096% optimization time cost compared with the MPC controller.https://www.mdpi.com/1996-1073/12/13/2587data predictive controlneural networkenergy management |
| spellingShingle | Suyang Zhou Fenghua Zou Zhi Wu Wei Gu Potential of Model-Free Control for Demand-Side Management Considering Real-Time Pricing Energies data predictive control neural network energy management |
| title | Potential of Model-Free Control for Demand-Side Management Considering Real-Time Pricing |
| title_full | Potential of Model-Free Control for Demand-Side Management Considering Real-Time Pricing |
| title_fullStr | Potential of Model-Free Control for Demand-Side Management Considering Real-Time Pricing |
| title_full_unstemmed | Potential of Model-Free Control for Demand-Side Management Considering Real-Time Pricing |
| title_short | Potential of Model-Free Control for Demand-Side Management Considering Real-Time Pricing |
| title_sort | potential of model free control for demand side management considering real time pricing |
| topic | data predictive control neural network energy management |
| url | https://www.mdpi.com/1996-1073/12/13/2587 |
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