A reduced-dimension feature extraction method to represent retail store electricity profiles
Characterising the inter-seasonal energy performance of buildings is a useful tool for a business to understand what is ‘normal’ for its portfolio of premises and to detect anomalous patterns of energy demand. When adding a new building to the portfolio, it will be useful to predict what will be the...
Main Authors: | Granell, R, Axon, CJ, Kolokotroni, M, Wallom, DCH |
---|---|
Format: | Journal article |
Language: | English |
Published: |
Elsevier
2022
|
Similar Items
-
Predicting electricity demand profiles of new supermarkets using machine learning
by: Granell, R, et al.
Published: (2020) -
Feature extraction to characterise and cluster the energy demand of UK retail premises
by: Granell, R, et al.
Published: (2015) -
A data-driven approach for electricity load profile prediction of new supermarkets
by: Granell, R, et al.
Published: (2019) -
Using existing building stock to predict the electricity load profiles of new supermarkets
by: Granell, R, et al.
Published: (2018) -
Impacts of raw data temporal resolution using selected clustering methods on residential electricity load profiles
by: Granell, R, et al.
Published: (2015)