# Decision Support Application for Energy Consumption Forecasting  
[PERSON]  
1 GECAD-Research Group on Intelligent Engineering and Computing for Advanced Innovation and Development, Institute of Engineering, Polytechnic of Porto (ISEP/IPP), 4249-015 Porto, Portugal; [EMAIL_ADDRESS] (A.J. ); [EMAIL_ADDRESS] (I.P.) [PERSON]  
1 GECAD-Research Group on Intelligent Engineering and Computing for Advanced Innovation and Development, Institute of Engineering, Polytechnic of Porto (ISEP/IPP), 4249-015 Porto, Portugal; [EMAIL_ADDRESS] (A.J. ); [EMAIL_ADDRESS] (I.P.) [PERSON]  
1 GECAD-Research Group on Intelligent Engineering and Computing for Advanced Innovation and Development, Institute of Engineering, Polytechnic of Porto (ISEP/IPP), 4249-015 Porto, Portugal; [EMAIL_ADDRESS] (A.J. ); [EMAIL_ADDRESS] (I.P.) [PERSON]  
2 Polytechnic of Porto (ISEP/IPP), 4249-015 Porto, Portugal; [EMAIL_ADDRESS] (A. Due to the amount of the energy consumption in buildings, prediction of this consumption is significant to improve the performance of energy systems, have better control on the energy distribution network, and reduce this amount of energy consumption. On the other hand, the energy system in buildings can be quite complex, depending on the types of building and types of energy consumers. Most of the considered building types are office, residential, and engineering buildings, varying from small rooms to big estates [7]. The consumption of a building is mostly divided into lights, HVAC, and electrical sockets; therefore, many different factors must be considered to have a proper recognition of the consumption behavior, such as environmental variables, number of users, etc. The complexity of energy consumption behavior in buildings makes it difficult to have a forecasting model to predict this behavior with acceptable performance. Many different studies have been published which propose various forecasting algorithms and models to predict the value of energy consumption. This approach can be categorized into statistical, engineering, and artificial intelligence models [4]. In the engineering methods, physical principles are used to calculate thermal dynamics and energy behavior on the whole building level or for sub-level components [7]. To have a good performance from these methods, a piece of high-level information about the structure of the building and different parameters are required, which might not be available. Moreover, to build a model which is based on complex physical principles, a high level of expertise is needed [8]. Statistical models are linear models based on statistical approaches, using historical data to obtain the relationships between data. Clearly, in these models, historical data plays an important role in acquiring a reliable result. Unlike the engineering models, in the statistical models, no physical structure is included [9]. The linear models consist of regression-type models, which are autoregressive (AR), autoregressive with exogenous variable (ARX), moving average (MA), autoregressive moving average (ARMA), autoregressive moving average with exogenous variable (ARMAX), and autoregressive integrated moving average (ARIMA) [10; 11; 12]. Artificial intelligence models, which are also known as nonlinear statistical models, can be categorized as Artificial Neural Networks (ANN), Support Vector Machines (SVMs), Fuzzy Rule-Based Systems (FRBS), and hybrid approaches [13; 14; 15]. Recently, these models have been widely used to solve many estimation problems. For example, in the study presented in [8], the author implements a neural network model to forecast the electricity demand of a bioclimatic building, located in the south-east of Spain. The results of this work show that this model can predict this value by an error of 11.48%. In [16], ANN is used to predict a day-ahead energy consumption profile for an office building. This work divides the total energy consumption of the building into three values, which present the consumption of lights, HVAC, and electrical sockets.