# Decision Support Application for Energy Consumption Forecasting
For each one of these three consumers, a specific forecasting model is created using external facility data, such as temperature and humidity. The results prove that this approach predicts the total energy consumption by an error of 13.6%. Many fuzzy rule-based methods have been purposed to be used in various forecasting proposes. In [17], five fuzzy rule-based methods, namely as [PERSON] and Mendel's method (WM), Hybrid neural Fuzzy Inference System (HyFIS), Genetic fuzzy systems for fuzzy rule learning based on the MOGUL methodology (GFS.FR.MOGUL), Genetic lateral tuning and rule selection of linguistic fuzzy systems (GFS.LT.RS), and the simplified TSK fuzzy rule-generation method using heuristics and gradient descent method (FS.HGD), are used to predict the hourly energy consumption of TRT-France buildings located in Paris, France. This study shows that in this case, the GFS.FR.MOGUL presents the most trustable results, followed by HyFIS and WM. In [18], a hybrid probabilistic fuzzy ARIMA model is presented, which is used to obtain a consumption forecasting in commodity markets. This hybrid model offers a combination of artificial intelligence models with linear statistical models where computational intelligence tools are used as well as soft computing techniques. The work presented in [19] proposes a model to forecast the energy consumption of public buildings to improve energy saving; Elman neural networks are used to make this prediction. Artificial intelligence models also can be used in other situation of forecasting. For example [20], a study has been presented that uses SVM to estimate the short-term wind speed where the results are also compared to the results of several ANN-based approaches. More studiesusing these forecasting approaches to solve different prediction and estimation problems can be found in [21; 22; 23]. Energy consumption estimation has an essential role in energy system management and the energy distribution network. However, to implement the forecasting models a high knowledge about programming languages, data storage systems, and data preparation is required. This fact can be a limitation for users working in this industry, which do not have this knowledge. In this way, this work proposes an application that makes it possible for the user to use different forecasting algorithms to predict the energy consumption of buildings. The proposed application, developed in the scope of the SIMOCE project (ANI I P2020 17690), is meant to be used by users who do not have enough knowledge to implement these methods and run the models on their own. Five forecasting methods are considered in this application, namely ANN, SVM, and three FRBS methods including, HyFIS, WM, and GFS.FR.MOGUL. Additionally, four different strategies to manage and prepare the data sets to train the methods are included. In this way, the users have access to several of the most relevant forecasting methods in this domain, as well as different approaches for data treatment and training. Besides facilitating the forecasting process for users with no experience in machine learning, the proposed application also enables mitigating the difficulties in choosing the best approaches for each prediction circumstance (amount of data, available data variables), as it provides the means to reach results with the different algorithms, and thus supports the user decisions in selecting the most suitable solution for each situation. In addition to the abovementioned benefits, which are mostly directed to users in the power and energy domain, the presented application also has direct advantages in multiple other domains, e.g., as facilitator for the implementation of energy management methods in manufacturing systems. By helping to reach suitable forecasts of energy consumption, the proposed application may be directly integrated with switch-off energy saving methods for production lines, such as the model proposed in [24]; or even integrated in advanced optimization methods for energy saving, as presented in [25].