Predicting Hourly Residential Energy Consumption using Random Forest and Support Vector Regression : An Analysis of the Impact of Household Clustering on the Performance Accuracy
William Hedén · 2016 · KTH Publication Database DiVA (KTH Royal Institute of Technology)
WASTE classifies this as Negative / Null Result Report · AI classification, approximate
The study found no significant effect — useful as a negative control or null benchmark for your own design.
Abstract (excerpt)
The recent increase of smart meters in the residential sector has lead to large available datasets. The electricity consumption of individual households can be accessed in close to real time, and allows both the demand and supply side to…
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