Search

This paper proposes a novel Case Based Reasoning (CBR) application for intelligent management of energy resources in residential buildings. The proposed CBR approach enables analyzing the history of previous cases of energy reduction in buildings, and using them to provide a suggestion on the ideal level of energy reduction that should be applied in the consumption of houses. The innovations of the proposed CBR model are the application of the k -Nearest Neighbors algorithm (k-NN) clustering algorithm to identify similar past cases, the adaptation of Particle Swarm Optimization (PSO) meta-heuristic optimization method to optimize the choice of the variables that characterize each case, and the development of expert systems to adapt and refine the final solution. A case study is presented, which considers a knowledge base containing a set of scenarios obtained from the consumption of a residential building. In order to provide a response for a new case, the proposed CBR application selects the most similar cases and elaborates a response, which is provided to the SCADA House Intelligent Management (SHIM) system as input data. SHIM uses this specification to determine the loads that should be reduced in order to fulfill the reduction suggested by the CBR approach. Results show that the proposed approach is capable of suggesting the most adequate levels of reduction for the considered house, without compromising the comfort of the users. [ABSTRACT FROM AUTHOR]

Abstract: Energy efficiency in buildings is essential for economic, social, and environmental reasons. Under current European legislation, buildings have to be certified with an energy efficiency index. To provide a fair comparison of the energy performance in buildings we need a global and unified representation. In this paper we propose the Energy Efficiency Ontology (EEOnt) to provide a foundation upon which this requirement can be built. EEOnt extends the applicability of other ontological models of buildings incorporating energy efficiency data and knowledge that can be calculated with high spatial and temporal resolution in what we call an Energy Efficiency Landscape (EELB). These EELB''s provide significant information which allows a detailed diagnosis of the energy efficiency within the building. Consequently, EEOnt is capable of representing buildings and energy efficiency in a unified way. Furthermore, it supplies a tool for managing the energy efficiency in buildings and constructing national inventories. These two features link the global and particular needs of governments and users, thus becoming key to both improve the energy efficiency in buildings and to implement viable certification schemes. [Copyright &y& Elsevier]