# Decision Support Application for Energy Consumption Forecasting
## 5 Conclusions  
This paper presents an automatic forecasting application based on Java and R programming languages. By using this application, the user can create different forecasting models using five forecasting methods and four data preparation strategies to predict the hour-ahead energy consumption of an office building. ANN, SVM, HyFIS, WM, and GFS.FR.MOGUL are the considered forecasting algorithms in this work as well as four data preparation strategies which are explained in Section 2. The most important advantage that this application offers is that the user does not need to have knowledge about the programming languages, forecasting technics, and data storage systems and all the process is done by the application. Additionally, by making different algorithms and data treatment strategies available, the proposed work also enables the user to find the best method to be used in distinct prediction conditions. This is especially relevant considering that by being domain-agnostic, the proposed application can be applied to multiple application domains, such as manufacturing systems, health related management systems, internet-of-things problems, or many other problems that require forecasting solutions based on historical data regression.  
The presented case studies using this application show that the proposed approaches in this work can forecast the hour-ahead energy consumption with acceptable performance. The SVM with the third proposed data preparation strategy presents an error of 4.35% which is a trustable performance compared to the other methods and previous work. Moreover, when using additional information for training, in this case, the temperature, as input for consumption forecasting, it is visible that both the HyFIS and WM can improve their results and reach better results than the SVM (in specific, using the 4 th data strategy). Hence, the application can support users' decisions on the best method, parameterization, and data treatment that should be used depending on the available data.  
As future work, further forecasting methods will be included, to broaden the available learning capabilities from various methods. Probabilistic forecasting will also be included, as well as the possibility of working with other types of data and databases, not necessarily related to energy consumption.  
[PERSON] conceived and designed the computational models, [PERSON], [PERSON], and [PERSON] conceived and designed the experiments; [PERSON] performed the experiments; [PERSON], [PERSON] and [PERSON] analyzed the data; [PERSON] contributed with expertise in AI methods for power systems; [PERSON] contributed with expertise in machine learning; [PERSON] wrote the paper; [PERSON], [PERSON] and [PERSON] revised and improved the paper.  
The present work has been developed under Project SIMOCE (ANI I P2020 17690) co-funded by Portugal 2020 \"Fundo Europeu de Desenvolvimento Regional\" (FEDER) through COMPETE, the EUREKA  
Figure 16: Average MAPE errors of the forecasting methods.  
- ITEA2 Project M2 MGrids (ITEA-13011), and has received funding from FEDER Funds through COMPETE program and from National Funds through FCT under the project UID/EEA/00760/2019.  
The authors declare no conflict of interest.