| Full text | |
| Author(s): |
Bessani, Michel
;
Massignan, Julio A. D.
;
Santos, Talysson M. O.
;
London Jr, Joao B. A.
;
Maciel, Carlos D.
Total Authors: 5
|
| Document type: | Journal article |
| Source: | Electric Power Systems Research; v. 189, p. 7-pg., 2020-12-01. |
| Abstract | |
Load forecasting is essential for different activities on power systems, and there is extensive research on approaches for forecasting in different time horizons, from next-hour to decades. However, because of higher uncertainty and variability compared to aggregated or medium and high voltage, the forecasting of the individual household load is a current challenge. This paper presents a load forecasting for multiple households using Bayesian networks. Our model, which is multivariate, uses past consumption, temperature, socioeconomic and electricity usage aspects to forecast the next instant household load value. It was tested using real data from the Irish smart meter project and its performance was compared with other forecasting methods. Results have shown that the proposed approach provides consistent single forecast model for hundreds of households with different consumption patterns, showing a generalisation capability in an efficient manner. (AU) | |
| FAPESP's process: | 16/19646-6 - Three-phase Multiarea state estimator for large scale distribution systems |
| Grantee: | Julio Augusto Druzina Massignan |
| Support Opportunities: | Scholarships in Brazil - Doctorate |
| FAPESP's process: | 14/50851-0 - INCT 2014: National Institute of Science and Technology for Cooperative Autonomous Systems Applied in Security and Environment |
| Grantee: | Marco Henrique Terra |
| Support Opportunities: | Research Projects - Thematic Grants |
| FAPESP's process: | 18/00214-4 - Distribution systems state estimators: classical approaches and new algorithms for the Smart Grid |
| Grantee: | Julio Augusto Druzina Massignan |
| Support Opportunities: | Scholarships abroad - Research Internship - Doctorate |