Predicting electricity energy consumption: A comparison of regression analysis, decision tree and neural networks

By Geoffrey Kwok Fai Tso, Kelvin K. W. Yau. 2007

976 citations recorded by the source corpus.

Subfields: Artificial Intelligence, Electrical and Electronic Engineering

View Paper: https://www.semanticscholar.org/paper/72a8c9d2756efe1eef9a04e03d0fe658451f536e

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Citations in the corpus

The socio-economic, dwelling and appliance related factors affecting electricity consumption in domestic buildings (2015, methodology): “Common data collection methods used in dwelling level studies are personal interviews [8,20], phone surveys [21], electricity meter readings provided by energy providers [14,22,23], household electricity monitoring [5,12,21,24], including submetering of appliances [20,25], questionnaires [7,12,20,23”
Do homes that are more energy efficient consume less energy?: A structural equation model for England's residential sector (2011, methodology): “For example, a neural-network based national energy consumption model has been developed for the Canadian residential sector (Aydinalp et al. 2003) and other innovative statistical techniques such as decision tree analyses are being used to model residential energy consumption at the national level ”
State-of-the-art review of soft computing applications in underground excavations (2020, methodology): “These models generate a set of rules which can be used for prediction through the repetitive process of splitting (Tso and Yau, 2007).”
Targeting energy justice: Exploring spatial, racial/ethnic and socioeconomic disparities in urban residential heating energy efficiency (2016, background): “specific end use consumption, total consumption, energy use intensity) while controlling for exogenous variables such as housing unit characteristics, household characteristics, urban form and climatic conditions (Min et al., 2010; Ewing and Rong, 2008; Tso and Yau, 2007).”
Forecast of electricity consumption and economic growth in Taiwan by state space modeling (2009, methodology): “On the other hand, the multivariate models of neural network techniques [29,30], regression and econometric approach [31–33] have been also applied in predicting EC.”
Determinants of high electrical energy demand in UK homes: Socio-economic and dwelling characteristics (2015, positive): “This finding is consistent with a number of previous studies [11,16,29].”