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Abstract
The energy efficiency of buildings is a key condition in the implementation of national sustainability policies. Energy efficiency of the built heritage is usually achieved through energy contracts or renovation projects that are based on decisions often taken with limited knowledge and in short time frames. However, the collection of comprehensive and reliable technical information to support the decision process is a long and expensive activity. Approximate assessment methods based on stationary thermal models are usually adopted, often introducing unacceptable uncertainties for economically onerous contracts. Hence, it is important to develop tools that, by capitalizing on the operators’ experience, can provide support for fast and reliable assessments. The paper documents the development of a decision support system prototype for the management of energy refurbishment investments in the residential building sector that assists operators in the energy performance assessment, using a limited set of technical information. The system uses a Case Based paradigm enriched with probabilistic modelling to implement decision support within the corporate’s knowledge management framework.
Highlights
Europe is a continent with a very well-established building stock, which is characterized by large energy renovation needs, accounting for almost 50% of the building market [1].Increasing the buildings’ energy efficiency is the subject of the Energy Performance of Buildings Directive (EPBD) [2,3]
Naive Bayes Classifier (NBC) is a Bayesian network with a tree topology (Figure 8), made of a root node whose domain is formed by the set of possible classes, connected to a set of leaf nodes representing the parameters whose values characterize the different classes
This paper discussed the architecture of a decision support system for energy refurbishment of buildings
Summary
Europe is a continent with a very well-established building stock, which is characterized by large energy renovation needs, accounting for almost 50% of the building market [1]. Current decision support tools are based on large stock analyses of buildings and are well suited for the strategic management of real estate investment [8,9] These systems have been developed to speed-up the audit process in the context of large-scale energy efficiency policies. The reduced order model, relatively abstract, is sufficiently expressive to produce energy performance predictions within ASHRAE accuracy boundaries [24]; The model can be implemented according to information obtained from inexpensive surveys, mostly based on visual inspection; The calibration process provides support in assessing the consistency of the surveyed data. A Bayesian probabilistic model is used to mine the knowledge repository and to implement an index frame that provides decision support Such a system can estimate building energy performance with different accuracy degree, depending on the available information, either through simulation or CBR.
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