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Open AccessArticle

Energy Performance Forecasting of Residential Buildings Using Fuzzy Approaches

Soft Computing Research Group at Intelligent Data Science and Artificial Intelligence Research Center, Universitat Politècnica de Catalunya—BarcelonaTech, Jordi Girona Salgado 1-3, 08034 Barcelona, Spain
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Appl. Sci. 2020, 10(2), 720; https://doi.org/10.3390/app10020720
Received: 3 November 2019 / Revised: 15 December 2019 / Accepted: 16 January 2020 / Published: 20 January 2020
(This article belongs to the Special Issue Machine Learning for Energy Forecasting)
The energy consumption used for domestic purposes in Europe is, to a considerable extent, due to heating and cooling. This energy is produced mostly by burning fossil fuels, which has a high negative environmental impact. The characteristics of a building are an important factor to determine the necessities of heating and cooling loads. Therefore, the study of the relevant characteristics of the buildings, regarding the heating and cooling needed to maintain comfortable indoor air conditions, could be very useful in order to design and construct energy-efficient buildings. In previous studies, different machine-learning approaches have been used to predict heating and cooling loads from the set of variables: relative compactness, surface area, wall area, roof area, overall height, orientation, glazing area and glazing area distribution. However, none of these methods are based on fuzzy logic. In this research, we study two fuzzy logic approaches, i.e., fuzzy inductive reasoning (FIR) and adaptive neuro fuzzy inference system (ANFIS), to deal with the same problem. Fuzzy approaches obtain very good results, outperforming all the methods described in previous studies except one. In this work, we also study the feature selection process of FIR methodology as a pre-processing tool to select the more relevant variables before the use of any predictive modelling methodology. It is proven that FIR feature selection provides interesting insights into the main building variables causally related to heating and cooling loads. This allows better decision making and design strategies, since accurate cooling and heating load estimations and correct identification of parameters that affect building energy demands are of high importance to optimize building designs and equipment specifications. View Full-Text
Keywords: energy performance; heating and cooling load; Fuzzy Inductive Reasoning (FIR); Adaptive Neuro-Fuzzy Inference System (ANFIS) energy performance; heating and cooling load; Fuzzy Inductive Reasoning (FIR); Adaptive Neuro-Fuzzy Inference System (ANFIS)
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MDPI and ACS Style

Nebot, À.; Mugica, F. Energy Performance Forecasting of Residential Buildings Using Fuzzy Approaches. Appl. Sci. 2020, 10, 720. https://doi.org/10.3390/app10020720

AMA Style

Nebot À, Mugica F. Energy Performance Forecasting of Residential Buildings Using Fuzzy Approaches. Applied Sciences. 2020; 10(2):720. https://doi.org/10.3390/app10020720

Chicago/Turabian Style

Nebot, Àngela; Mugica, Francisco. 2020. "Energy Performance Forecasting of Residential Buildings Using Fuzzy Approaches" Appl. Sci. 10, no. 2: 720. https://doi.org/10.3390/app10020720

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