Development of surplus power generation forecast for use by residential loads

Dias, Paloma Greiciana de Souza; Brito, Thadeu; Silva, William Rodrigues; Pereira, Ana I.; Lopes, Luis C.G.; Santos, Murillo F. dos; Costa, Paulo Gomes da; Lima, José

http://hdl.handle.net/10198/29137

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Authors

Dias, Paloma Greiciana de Souza

Brito, Thadeu

Silva, William Rodrigues

Pereira, Ana I.

Lopes, Luis C.G.

Santos, Murillo F. dos

Costa, Paulo Gomes da

Lima, José

Abstract(s)

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.

Keywords

Surplus energy Data forecasting Machine learning Internet of things

URI

http://hdl.handle.net/10198/29137

Citation

Dias, Paloma Greiciana de Souza; Brito, Thadeu; Silva, William R.; Pereira, Ana I.; Lopes, Luis C.G.; Santos, Murillo F. dos; Costa, Paulo Gomes da; Lima, José (2023). Development of surplus power generation forecast for use by residential loads. In 2023 3rd International Conference on Electrical, Computer, Communications and Mechatronics Engineering (ICECCME). 19-21 July 2023, Tenerife. p. 1-6. ISBN 979-8-3503-2297-2