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| Content Provider | IET Digital Library |
|---|---|
| Author | Crețu, Mihaela Czumbil, Levente Bârgăuan, Bogdan Ceclan, Andrei Berciu, Alexandru Polycarpou, Alexis Rizzo, Renato Micu, Dan D. |
| Abstract | Demand response (DR) programmes offer to customers the opportunity to reduce the power peak and the energy consumption in response to a price signal or financial incentive. Typically, the request to reduce peak demands is made for a specific time period on a specific day, which is referred to as a DR event. To predict a reference energy consumption level in case of different buildings or blocks of buildings within the Technical University of Cluj-Napoca, this study proposes an artificial intelligence enhanced energy profiling method and a more intuitive yet simple method for baseline determination, easy to understand, which allows all the interested parties to estimate the energy and economy savings after a DR event. Once the baseline electric load profile is established, the aim of this study is to calculate some predefined key performance indicators. The two baseline detection methods are compared with each other as a measure of DR event effectiveness. The study has been conducted to clearly demonstrate the economic and environmental benefits of controlling the aggregated load curve in blocks of buildings within several effectively applied DR programmes. |
| Starting Page | 2864 |
| Ending Page | 2875 |
| Page Count | 12 |
| ISSN | 17521416 |
| Volume Number | 14 |
| e-ISSN | 17521424 |
| Issue Number | Issue 15, Nov (2020) |
| Alternate Webpage(s) | https://digital-library.theiet.org/content/journals/iet-rpg/14/15 |
| Alternate Webpage(s) | https://digital-library.theiet.org/content/journals/10.1049/iet-rpg.2020.0096 |
| Journal | IET Renewable Power Generation |
| Publisher | The Institution of Engineering and Technology |
| Publisher Date | 2020-10-27 |
| Access Restriction | Open |
| Rights License | Creative Commons Attribution-Non Commercial-No Derivs License (http://creativecommons.org/licenses/by-nc-nd/3.0/) |
| Subject Keyword | Aggregated Load Curve Artificial Intelligence Enhanced Energy Profiling Method Baseline Detection Method Baseline Determination Baseline Electric Load Profile Baseline Energy Consumption Building Management System Buildings (energy Utilisation) Demand Response Programme Demand Side Management DR Event Effectiveness DR Programmes Economy Savings Educational Institutions Energy Consumption Financial Incentive Power System Economics Power System Managemen Power System Operation Predefined Key Performance Indicators Price Signal Pricing Reference Energy Consumption Level Specific Time Period Technical University of Cluj-Napoca Buildings |
| Content Type | Text |
| Resource Type | Article |
| Subject | Renewable Energy, Sustainability and the Environment |
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