Name
Abstract | ||
|---|---|---|
Demand peaks in electrical power system cause serious challenges for energy providers as these events are typically difficult to foresee and require the grid to support extraordinary consumption levels. Accurate peak forecasting enables utility providers to plan the resources and also to take control actions to balance electricity supply and demand. However, this is difficult in practice as it requires precision in prediction of peaks in advance. In this paper, our contribution is the proposal of data mining scheme to detect the peak load in the electricity system at country level. For this purpose we undertake the approach different from time series forecasting and represent it as pattern recognition problem. We utilize set of artificial neural networks to benefit from accurate detection of the peaks in the Polish power system. The key finding is that the algorithms can accurately detect 96.2% of the electricity peaks up to 24 hours ahead. |
Year | DOI | Venue |
|---|---|---|
2017 | 10.15439/2017F168 | 2017 Federated Conference on Computer Science and Information Systems (FedCSIS) |
Keywords | Field | DocType |
peak load,electricity system,country level,time series forecasting,artificial neural networks,accurate detection,Polish power system,electricity peaks,electricity peak demand classification,demand peaks,electrical power system,utility providers,electricity supply,data mining scheme,peak forecasting | Electric power,Data mining,Time series,Electricity,Computer science,Electric power system,Peak demand,Mains electricity,Artificial intelligence,Artificial neural network,Machine learning,Grid | Conference |
ISSN | ISBN | Citations |
2325-0348 | 978-1-5090-4414-6 | 1 |
PageRank | References | Authors |
0.63 | 2 | 3 |
Name | Order | Citations | PageRank |
|---|---|---|---|
| Krzysztof Gajowniczek | 1 | 19 | 6.14 |
| Rafik Nafkha | 2 | 1 | 0.63 |
| Tomasz Zabkowski | 3 | 32 | 11.28 |