Name
Abstract | ||
|---|---|---|
Recent changes in the power systems gives place to the active consumers participation. The participation in demand response programs requires consumers to undertake strategic management of their consumption. Small and medium players should have the capability of performing day-ahead and hour-ahead load management which requires forecasting techniques applied to the consumption and generation. A good forecasting accuracy is very important for the quality of the management results but also very difficult to achieve. This paper proposes an artificial neural network based methodology to forecast the consumption in an office building. The considered building is equipped with a Supervisory Control and Data Acquisition (SCADA) system that stores data every 10 seconds. The stored data are used together with additional data, such as, the temperature and the solar radiation. |
Year | DOI | Venue |
|---|---|---|
2015 | 10.1109/SSCI.2015.101 | 2015 IEEE SYMPOSIUM SERIES ON COMPUTATIONAL INTELLIGENCE (IEEE SSCI) |
Keywords | Field | DocType |
forecasting,artificial neural networks | Load management,Electrical energy consumption,Simulation,Computer science,Demand response,Electric power system,Strategic management,SCADA,Artificial neural network,Reliability engineering | Conference |
Citations | PageRank | References |
4 | 0.59 | 5 |
Authors | ||
3 | ||
Name | Order | Citations | PageRank |
|---|---|---|---|
| Eugénia Vinagre | 1 | 5 | 2.63 |
| Luís Gomes | 2 | 18 | 7.38 |
| Zita Vale | 3 | 64 | 27.90 |