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Neural Processing Letters
Erschienen in: Neural Processing Letters 1/2018

14.10.2017

Small-Scale Building Load Forecast based on Hybrid Forecast Engine

verfasst von: Mohsen Mohammadi, Faraz Talebpour, Esmaeil Safaee, Noradin Ghadimi, Oveis Abedinia

Erschienen in: Neural Processing Letters | Ausgabe 1/2018

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Abstract

Electricity load forecasting plays an important role for optimal power system operation. Accordingly, short term load forecast (STLF) is also becoming an important task by researchers to tackle the mentioned problem. As a consequence of the highly non-smooth and volatile trend of the load time series specially in building levels, its STLF is even a more complex procedure than that of a power system. For this purpose, in this paper we proposed a new prediction model based on a new feature selection algorithm and hybrid forecast engine of enhanced version of empirical mode decomposition named sliding window EMD bundled with an intelligent algorithm. The proposed forecast engine is combined with novel shark smell optimization to increase the prediction accuracy. All weights of this forecast engine have been optimized with an intelligent algorithm to find better prediction results. Effectiveness of the proposed model is carried out to real-world engineering test case in comparison with other prediction models.
Vorheriger Artikel A New Virtual Samples-Based CRC Method for Face Recognition
Nächster Artikel Fitness Landscape Analysis of Weight-Elimination Neural Networks

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Metadaten
Titel
Small-Scale Building Load Forecast based on Hybrid Forecast Engine
verfasst von
Mohsen Mohammadi
Faraz Talebpour
Esmaeil Safaee
Noradin Ghadimi
Oveis Abedinia
Publikationsdatum
14.10.2017
Verlag
Springer US
Erschienen in
Neural Processing Letters / Ausgabe 1/2018
Print ISSN: 1370-4621
Elektronische ISSN: 1573-773X
DOI
https://doi.org/10.1007/s11063-017-9723-2

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