Detailed analysis of a deep learning energy forecast model considering different input units and magnitudes
The conference paper “Detailed analysis of a deep learning energy forecast model considering different input units and magnitudes” was presented in the 12th IFAC Symposium on Control of Power & Energy Systems. The conference held in Rabat, Morocco, took place in July 2024. ISEP's participation allowed the dissemination of knowledge acquired and explored within the scope of the PRODUTECH R3 – WP13 project.
The presentation aimed to present the study carried out on energy forecasting models, taking into account the impact caused by manipulating outputs, and consequently adjusting inputs.
The study allowed the evaluation of predictions made using the same database, but using various forms of data representation, such as adjusting the magnitude of variables.
The results demonstrate that the units and dimensions of the inputs have an impact on the prediction error, indicating that the accumulated Wh values are potentially better for the energy prediction model used.
The results of this study make it possible to increase the accuracy of energy forecasting algorithms, which will be extremely important for the application of energy optimization models using production schedules. This will make it possible to increase the production efficiency of the WP13 pilots of the PRODUTECH R3 project.
This publication by the ISEP team, Bruno Ribeiro, Rafael Silva, Luis Gomes, and Zita Vale, is now available online on https://www.sciencedirect.com/science/article/pii/S2405896324005998
This work has been supported by the European Union under the Next Generation EU, through a grant of the Portuguese Republic's Recovery and Resilience Plan (PRR) Partnership Agreement, within the scope of the project PRODUTECH R3 – "Agenda Mobilizadora da Fileira das Tecnologias de Produção para a Reindustrialização".