# Energy Performance Forecasting of Residential Buildings Using Fuzzy Approaches
## 1 Introduction  
In recent years there has been a substantial increase of research in the area of energy performance in buildings. The aim is to design and construct more energy-efficient buildings in order to reduce their energy consumption and CO2 emissions. Buildings are responsible for approximately 40% of energy consumption and 36% of CO2 emissions in the EU, making them the single largest energy consumer in Europe. Residential buildings comprise the biggest segment of the EU's building stock and are responsible for the majority of the sector's energy consumption [1]. The European Commission (EC) boosted the research in this area with a program framed in Horizon 2020 [1]. In the Energy Efficiency Plan in Europe, the EC states that a greatest energy saving potential lies in buildings. The minimum energy savings in buildings can generate a reduction of 60-80 Mtoe in final energy consumption by 2020, and make a considerable contribution to the reduction of greenhouse gas (GHG) emissions [2]. Machine-learning (ML) strategies, within the field of artificial intelligence, have already been used to deal with estimation and performance of energy consumption in buildings, as summarized in therecent review by [PERSON] et al. [3]. The main ML techniques widely used in the energy building sector are artificial neural network (ANN) and support vector machines (SVM). ANN models have been applied mainly for fast estimation of heating and cooling loads [4], energy consumption and efficiency [5; 6] and optimization and control of heating systems [7; 8]. In the building sector, SVM have been mainly used for classification of energy usage [9], cooling and heating loads forecasting [10] and prediction of energy consumption [11]. Other ML and soft computing techniques, as for example genetic algorithms (GA) or random forest (RF), have also been used in this area to a lesser extent [12; 13]. Contrarily, up to our knowledge, Fuzzy logic based methods have been applied sparingly to the energy performance estimation of buildings. Fuzzy logic approaches, within the analysis of energy in buildings, have been used mainly in the control area [14] and in multiple criteria decision-making [15]. However, up to our knowledge, Fuzzy logic based methods have been applied sparingly to the energy performance estimation of buildings. In this research we propose to apply fuzzy logic modelling for energy performance forecasting of residential buildings. In the building energy field, the use of fuzzy logic modelling approaches can be a very good alternative due to its potentiality to work with uncertainties, to explain effectively complex relationships and to understand uncertain causal relations between variables. In addition, its capacity to obtain high accuracy predictions is of great importance when dealing with energy consumption. These reasons encourage us to make an exploration of non-linear fuzzy approaches, i.e., fuzzy inductive reasoning (FIR) and adaptive neuro fuzzy inference system (ANFIS), for estimating energy performance of residential buildings. A first attempt in this direction has been presented in reference [16]. [PERSON] and [PERSON] [17] generated a set of simulated residential buildings that are available in the UCI machine-learning repository [18]. It is important to remark that the use of simulated building data is useful and justified when dealing with energy prediction challenges, since it allows researchers to reduce considerably the time needed to obtain the data, design easily different building configurations and work with a lower level of detail. As claimed by the authors, the data generated represent with high probability actual real data, enabling energy comparisons between buildings. In their research, classical linear regression and random forest approaches were used to predict the heating and cooling load of residential buildings [17].