Energy Performance Forecasting of Residential Buildings Using Fuzzy Approaches

Abstract

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.

Application of the Takagi-Sugeno Fuzzy Modeling to Forecast Energy Efficiency in Real Buildings Undergoing Thermal Improvement

“…Literature analysis has shown that statistical and artificial neural network-based models are most commonly used, while fuzzy logic-based models are used much less frequently. A fuzzy approach for determining the energy performance of buildings was applied by Nebot and Mugica [34], where two methods were used for prediction: fuzzy inductive inference (FIR) and adaptive neuro-fuzzy inference system (ANFIS). The study was performed on a set of simulated residential buildings developed by Tsanas and Xifara [30].…”

“…The study was performed on a set of simulated residential buildings developed by Tsanas and Xifara [30]. The computational results obtained by the authors [34] have been compared with the results obtained for the same input data (for simulated buildings) using static and artificial neural networkbased methods [25,28,30,31]. The results showed that the use of methods based on fuzzy logic gives very good results in predicting energy consumption [34].…”

A Review of Energy Consumption Forecasting in Smart Buildings: Methods, Input Variables, Forecasting Horizon and Metrics

“…Using a hybrid method frequently requires long computation time because of the boundary advancement procedure and master information during the model improvement process [84]. There have been studies on hybrid methods that have focused on electrical consumption [85][86][87][88][89][90][91][92], cooling and heating load [79,80,[93][94][95], energy consumption [96][97][98][99][100][101], thermal load [102], thermal response [103] and load demand [104]. Based on the aforementioned studies, a summary of their contributions and limitations is presented in Table 3.…”

Sensor-Data-Driven Prognosis Approach of Liquefied Natural Gas Satellite Plant

“…Bose et al [17] reviewed the contribution in the area of Fuzzy time series (FTS) forecasting, these models have proven to be a powerful tool for modelling and forecasting complex systems in different domains including energy, economics, biology, etc. One of this technique is the Fuzzy Inductive Reasoning (FIR), which has used for energy forecasting in residential buildings and smart grid [18,19].…”