Huifen Zhou
ABSTRACT
The impacts of weather attributes on commercial and residential electricity demands and their components in the northwestern United States were examined. Two machine learning methods, regression tree (RT), and random forest (RF), were integrated and compared. Both RT and RF models provide reliable predictions of commercial cooling load. RF models particularly yield higher accuracy with reduced overfitting.
- B. Yildiz, J. I. Bilbao, and A. B. Sproul, "A review and analysis of regression and machine learning models on commercial building electricity load forecasting," Renewable and Sustainable Energy Reviews, vol. 73, pp. 1104--1122, 2017.Google Scholar
Cross Ref
- L. Breiman, Classification and regression trees: Routledge, 2017.Google ScholarCross Ref
- L. Breiman, "Random forests," Machine learning, vol. 45, no. 1, pp. 5--32, 2001. Google ScholarDigital Library
Index Terms
- Machine Learning of Commercial and Residential Load Components in the Northwestern United States
Recommendations
Urban Residential Power Load Risk Identification Based on Data Mining
FSKD '09: Proceedings of the 2009 Sixth International Conference on Fuzzy Systems and Knowledge Discovery - Volume 02Following the fast urban economic development and the improvement of people living standard, urban residential electricity consumption is increasing quickly. Although the influencing factors of urban residential power demand are complex, objective ...
Urban residential power load risk identification based on data mining
FSKD'09: Proceedings of the 6th international conference on Fuzzy systems and knowledge discovery - Volume 2Following the fast urban economic development and the improvement of people living standard, urban residential electricity consumption is increasing quickly. Although the influencing factors of urban residential power demand are complex, objective ...
New machine learning algorithm: random forest
ICICA'12: Proceedings of the Third international conference on Information Computing and ApplicationsThis Paper gives an introduction of Random Forest. Random Forest is a new Machine Learning Algorithm and a new combination Algorithm. Random Forest is a combination of a series of tree structure classifiers. Random Forest has many good characters. ...
Comments