Data mining and machine learning methods aimed at predicting the electrical energy demand of air conditioning systems are described with reference to a real-time control of these systems in smart buildings. The case study presented is applicable to higher-complexity systems, which may include several prosumer nodes, i.e. nodes that have to generate and implement strategies of energy supply, trade, consumption and storage. These nodes are connected to a smart grid and remotely supervised by a Distributed System Operator (DSO) using distributed control and monitoring systems. Accurate, continuously-recorded local weather data are used to predict energy demand in order to make decisions aimed at both reducing energy consumption and assuring pre-established comfort levels. Energy savings can be estimated by observing a buildings energy performance under the action of different meteorological agents. The proposed, real-time estimation architecture aims at exploiting a buildings intrinsic thermal inertia in order to anticipate energy demand whenever more convenient contextual conditions are met. The suggested on-line management system was validated through a laboratory experimental test site, and results are reported and discussed. © 2015, UK Simulation Society. All rights reserved.

The impact of energy demand prediction on the automation of smart buildings management

Muzi Francesco;De Gasperis Giovanni
2015-01-01

Abstract

Data mining and machine learning methods aimed at predicting the electrical energy demand of air conditioning systems are described with reference to a real-time control of these systems in smart buildings. The case study presented is applicable to higher-complexity systems, which may include several prosumer nodes, i.e. nodes that have to generate and implement strategies of energy supply, trade, consumption and storage. These nodes are connected to a smart grid and remotely supervised by a Distributed System Operator (DSO) using distributed control and monitoring systems. Accurate, continuously-recorded local weather data are used to predict energy demand in order to make decisions aimed at both reducing energy consumption and assuring pre-established comfort levels. Energy savings can be estimated by observing a buildings energy performance under the action of different meteorological agents. The proposed, real-time estimation architecture aims at exploiting a buildings intrinsic thermal inertia in order to anticipate energy demand whenever more convenient contextual conditions are met. The suggested on-line management system was validated through a laboratory experimental test site, and results are reported and discussed. © 2015, UK Simulation Society. All rights reserved.
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Utilizza questo identificativo per citare o creare un link a questo documento: https://hdl.handle.net/11697/120843
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