Detalhes bibliográficos
| Resumo: | Energy consumption has been increasing in the last years and thus, energy efficiency is one of the most important topics actually. Besides, the consumption and energy generation forecast help in efficiency optimization. This paper presents the development of a system for forecasting surplus power generation to be used by residential loads connected to smart plugs. In this way, it is intended to collaborate with the use of surplus energy production in electrical devices in a residence instead of sending to batteries or to the grid. This work presents the theoretical basis of the project and the architecture of the developed system. A Machine Learning method applied to photovoltaic generation data in a residence was used to predict surplus energy. |
|---|
| Autores principais: | Dias, Paloma Greiciana de Souza |
|---|
| Outros Autores: | Brito, Thadeu; Silva, William Rodrigues; Pereira, Ana I.; Lopes, Luis C.G.; Santos, Murillo F. dos; Costa, Paulo Gomes da; Lima, José |
|---|
| Assunto: | Surplus energy Data forecasting Machine learning Internet of things |
|---|
| Ano: | 2023 |
|---|
| País: | Portugal |
|---|
| Tipo de documento: | comunicação em conferência |
|---|
| Tipo de acesso: | acesso restrito |
|---|
| Instituição associada: | Instituto Politécnico de Bragança |
|---|
| Idioma: | inglês |
|---|
| Origem: | Biblioteca Digital do IPB |
|---|