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Due to the significant contribution of air-conditioning load towards total energy consumption in residential buildings, accurate modelling and forecasting of such load is key to effective demand-side energy management programmes. This paper suggests a data driven framework for 15 min-ahead AC load forecasting based on modern machine learning techniques that includes Support Vector Regression, Ensemble...
In this paper, we propose and study the effectiveness of customer engagement plans that clearly specify the amount of intervention in customer’s load settings by the grid operator for peak load reduction. We suggest two different types of plans, including constant deviation plans (CDPs) and proportional deviation plans (PDPs). We define an adjustable reference temperature for both CDPs and PDPs to...
In recent years, many studies have investigated the potential of demand response management (DRM) schemes to manage energy for residential buildings in a smart grid. However, most of the existing studies mainly focus on the theoretical design of DRM schemes and do not verify the proposed schemes through implementation. Smart grid research is highly interdisciplinary. As such, the establishment of...
We develop a mathematical programming approach to schedule meetings in an organization over a fixed period of time, while minimizing the wasted energy and possibly achieving more balanced demand distribution. The problem is formulated as a mixed integer linear program subject to a set of realistic constraints including people's available time slots and energy consumption characteristics of the meeting...
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