A master thesis from Aalborg University
Scheduling of home appliances based on adaptive user optimization and diverse forecasting models.
Author(s)
Term
4. term
Education
Publication year
2021
Submitted on
2021-05-17
Pages
52 pages
Abstract
In United States the residential and commercial buildings consume 73% of the electricity. The Smart Grid implementations have grown boosting concepts such as: Demand Side Management (DSM), Advanced Metering (AM), Demand Response (DR) and Scheduling and Forecasting (SF). The renewable energy sources as wind turbines and photovoltaics (PV) behave uncertainly, therefore there is a gap between the supply and demand energy. To tackle the imbalances, many studies have proposed solutions based on DR strategies to reschedule the load energy. From this perspective to accomplish energy efficiency at household level, it is necessary to use the flexibility concept to adjust the supply demand gap. This project proposes to get the possible energy loads that can be rescheduled as flexible consumption descriptions (flex-offers). This work focuses on wet devices (washing machine, dishwasher) because they can change the behaviour to fit in the RES production energy and they represent 30% of household consumption. In Demand Side Management, the pricing mechanisms are designed to encourage the consumers to change their behaviour, for example the timeof- use pricing sets different prices during the day, hence the consumer change the demand to off-peak hours. In this context, to schedule the consumer loads, we have to apply the best machine learning models to get the best results.
Keywords
Documents
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