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Abstract
Recent studies show that energy consumption of buildings has dramatically increased over the last decade, accounting for more than 35% of global energy use. However, with proper operation, significant energy savings can be achieved. Demand response is envisioned as a key enabler of this operation enhancement, as it may contribute to the reduction of demand peaks and maximization of renewable energy exploitation while mitigating potential problems with grid stability. In this article, a system based on artificial intelligence that solves the complex multi-objective problem to bring demand response programs to the residential sector is proposed. Through the application of novel machine learning-based algorithms, a unique control loop is developed to help dwellers determine how and when to use their appliances. The feasibility and validity of the proposed system has been demonstrated in a real-world neighbourhood where a notable reduction and shift of electricity demand peaks has been achieved. Concretely, in accordance with extreme changes in the energy prices, the users have demonstrated the ability to shift their demand to periods with lower prices as well as reducing power consumption during periods with higher prices, thus fully translating the demand peak in time.
Highlights
Buildings’ energy consumption has dramatically increased over the last decade due to different factors including the population growth, the increase in time spent indoors or the increased demand for building functions and indoor life quality [1]
Recent studies show that energy consumption of buildings has dramatically increased over the last decade, accounting for more than 35% of global energy use
RESPOND aims to bring demand response (DR) programs to neighbourhoods across Europe by solving the solving the energy demand optimization considering the management of collectively shared renewable energy sources (RES) generation as well as variable pricing tariffs, specific demand flexibility constraints and dwellers comfort
Summary
Buildings’ energy consumption has dramatically increased over the last decade due to different factors including the population growth, the increase in time spent indoors or the increased demand for building functions and indoor life quality [1]. Solving the energy demand optimization for residential neighbourhoods that takes into account management of collectively shared energy assets such as RES generation as well as variable pricing tariffs and specific demand flexibility constraints is a complex multi-objective problem that requires the utilization of artificial intelligence (AI) systems. This is where the RESPOND H2020 project (https://project-respond.eu, accessed on 15 March 2021) originates, aiming to bring DR programs to neighborhoods across Europe.
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