Using the hierarchical temporal memory spatial pooler for short-term forecasting of electrical load time series
| Autor: | Emmanuel N. Osegi |
|---|---|
| Rok vydání: | 2020 |
| Předmět: |
Computer science
020209 energy Univariate 02 engineering and technology Bivariate analysis computer.software_genre Computer Science Applications Hierarchical temporal memory 03 medical and health sciences Electric power system 0302 clinical medicine Robustness (computer science) Outlier 0202 electrical engineering electronic engineering information engineering Kurtosis Data mining Time series computer 030217 neurology & neurosurgery Software Information Systems |
| Zdroj: | Applied Computing and Informatics. 17:264-278 |
| ISSN: | 2210-8327 2634-1964 |
| DOI: | 10.1016/j.aci.2018.09.002 |
| Popis: | In this paper, an emerging state-of-the-art machine intelligence technique called the Hierarchical Temporal Memory (HTM) is applied to the task of short-term load forecasting (STLF). A HTM Spatial Pooler (HTM-SP) stage is used to continually form sparse distributed representations (SDRs) from a univariate load time series data, a temporal aggregator is used to transform the SDRs into a sequential bivariate representation space and an overlap classifier makes temporal classifications from the bivariate SDRs through time. The comparative performance of HTM on several daily electrical load time series data including the Eunite competition dataset and the Polish power system dataset from 2002 to 2004 are presented. The robustness performance of HTM is also further validated using hourly load data from three more recent electricity markets. The results obtained from experimenting with the Eunite and Polish dataset indicated that HTM will perform better than the existing techniques reported in the literature. In general, the robustness test also shows that the error distribution performance of the proposed HTM technique is positively skewed for most of the years considered and with kurtosis values mostly lower than a base value of 3 indicating a reasonable level of outlier rejections. |
| Databáze: | OpenAIRE |
| Externí odkaz: |
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