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Full-Text Articles in Engineering
Coordinated Smart Home Thermal And Energy Management System Using A Co-Simulation Framework, Prateek Munankarmi
Coordinated Smart Home Thermal And Energy Management System Using A Co-Simulation Framework, Prateek Munankarmi
Electronic Theses and Dissertations
The increasing demand for electricity especially during the peak hours threaten the grid reliability. Demand response (DR), changing the load pattern of the consumer in response to system conditions, can decrease energy consumption during periods of high wholesale market price and also maintain system reliability. Residential homes consume 38% of the total electric energy in the U.S., making them promising for DR participation. Consumers can be motivated to participate in DR programs by providing incentives (incentive-based DR), or by introducing a time-varying tariff for electricity consumption (price-based DR). A home energy management system (HEMS), an automated system which can alter …
Energy Optimization And Coordination Frameworks For Smart Homes Considering Incentives From Discomfort And Market Analysis, Priti Paudyal
Energy Optimization And Coordination Frameworks For Smart Homes Considering Incentives From Discomfort And Market Analysis, Priti Paudyal
Electronic Theses and Dissertations
The electricity demand is increasing with the growing use of electricity-based appliances in today’s world. The residential sector’s electricity consumption share is also increasing. Demand response (DR) is a typical way to schedule consumers’ energy consumption and help utility to reduce the peak load demand. Residential demand management can contribute to reduce peak electric demand, decrease electricity costs, and maintain grid reliability. Though the demand management has benefits to the utility and the consumers, controlling the consumers electricity consumption provides inconvenience to the consumers. The challenge here is to properly address the customers’ inconvenience to encourage them to participate and …
Machine Learning For Load Profile Data Analytics And Short-Term Load Forecasting, Md. Rashedul Haq
Machine Learning For Load Profile Data Analytics And Short-Term Load Forecasting, Md. Rashedul Haq
Electronic Theses and Dissertations
Short-term load forecasting (STLF) is a key issue for the operation and dispatch of day ahead energy market. It is a prerequisite for the economic operation of power systems and the basis of dispatching and making startup-shutdown plans, which plays a key role in the automatic control of power systems. Accurate power load forecasting not only help users choose a more appropriate electricity consumption scheme and reduces a lot of electric cost expenditure but also is conducive to optimizing the resources of power systems. This advantage helps while improving equipment utilization for reducing the production cost and improving the economic …