Name
Abstract | ||
|---|---|---|
This paper presents the improved method for 24 h ahead load forecasting applied to individual household data from a smart metering system. In this approach we decompose a set of individual forecasts into basis latent components with destructive or constructive impact on the prediction. The main research problem in such model aggregation is the proper identification of destructive components that can be treated as some noise factors. To assess the randomness of signals and thus their similarity to the noise, we used a new variability measure that helps to compare decomposed signals with some typical noise models. The experiments performed on individual household electricity consumption data with blind separation algorithms contributed to forecasts improvements. |
Year | DOI | Venue |
|---|---|---|
2015 | 10.1007/978-3-319-26227-7_17 | PROCEEDINGS OF THE 9TH INTERNATIONAL CONFERENCE ON COMPUTER RECOGNITION SYSTEMS, CORES 2015 |
Field | DocType | Volume |
Constructive,Computer science,Load forecasting,Independent component analysis,Mains electricity,Artificial intelligence,Model aggregation,Blind signal separation,Metering mode,Machine learning,Randomness | Conference | 403 |
ISSN | Citations | PageRank |
2194-5357 | 0 | 0.34 |
References | Authors | |
2 | 3 | |
Name | Order | Citations | PageRank |
|---|---|---|---|
| Krzysztof Gajowniczek | 1 | 19 | 6.14 |
| Tomasz Zabkowski | 2 | 32 | 11.28 |
| Ryszard Szupiluk | 3 | 38 | 8.97 |