Scholarly open access journals, Peer-reviewed, and Refereed Journals, Impact factor 8.14 (Calculate by google scholar and Semantic Scholar | AI-Powered Research Tool) , Multidisciplinary, Monthly, Indexing in all major database & Metadata, Citation Generator, Digital Object Identifier(DOI)
Smart buildings can become more energy flexible with efficient short-term forecasting of electric power use and advanced demand-side management. This project highlights the importance of developing a supervised machine-learning model to forecast electric power usage in the short term. The study uses decision tree regression and random forest models to predict future power usage. Additionally, this project proposes a dynamic pricing model for the demand response system, which reduces end-user electricity costs and peak demand for the following day. To enable real-time monitoring and implement a demand response strategy, a smart grid system must be aware of the potential short-term electric power usage.
Keywords:
Machine learning, Internet of Things(IoT), Short-term forecasting ,Artificial Intelligence(AI) ,Demand-side Management.
Cite Article:
"AI-BASED POWERFLOW MANAGEMENT CONTROLLER FOR SMART BULIDING", International Journal for Research Trends and Innovation (www.ijrti.org), ISSN:2455-2631, Vol.8, Issue 5, page no.772 - 778, May-2023, Available :http://www.ijrti.org/papers/IJRTI2305121.pdf
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ISSN:
2456-3315 | IMPACT FACTOR: 8.14 Calculated By Google Scholar| ESTD YEAR: 2016
An International Scholarly Open Access Journal, Peer-Reviewed, Refereed Journal Impact Factor 8.14 Calculate by Google Scholar and Semantic Scholar | AI-Powered Research Tool, Multidisciplinary, Monthly, Multilanguage Journal Indexing in All Major Database & Metadata, Citation Generator