NDLI: Machine learning based disaggregation of air-conditioning loads using smart meter data

Content Provider	IET Digital Library
Author	Najafi, Behzad Narzo, Luca Di Rinaldi, Fabio Arghandeh, Reza
Abstract	This study proposes a novel machine learning-based methodology to estimate the air-conditioning (AC) load from the hourly smart meter data. The commonly employed approaches for disaggregating the share of the AC load from the total consumption are either using data obtained from dedicated sensors or high-frequency data that cannot be provided by conventional smart meters. In the present work, an alternative approach is proposed, in which a machine learning-based pipeline is first optimised and trained using the data obtained from a set of households in a period, including both smart meter data and the AC load measurements along with corresponding weather conditions. The obtained optimal pipeline is then utilised to estimate the AC load in another set of buildings in the same period of the year, while providing it with only the smart meter data and weather conditions. As the first step of the pipeline implementation, several features are extracted from the smart meters and weather data. The most promising algorithm is then determined, the corresponding hyper-parameters are optimised and the most influential parameters are finally determined. The proposed method leads to a lower monitoring system's cost, lower user privacy concerns and fewer data processing complexity compared to the conventional energy disaggregation approaches, while providing an acceptable accuracy.
Starting Page	4755
Ending Page	4762
Page Count	8
ISSN	17518687
Volume Number	14
e-ISSN	17518695
Issue Number	Issue 21, Nov (2020)
Alternate Webpage(s)	https://digital-library.theiet.org/content/journals/iet-gtd/14/21
Alternate Webpage(s)	https://digital-library.theiet.org/content/journals/10.1049/iet-gtd.2020.0698
Journal	IET Generation, Transmission & Distribution
Publisher Date	2020-08-13
Access Restriction	Open
Rights Holder	© The Institution of Engineering and Technology
Subject Keyword	AC Load Measurement Air Conditioning Air-conditioning Load Building Structures Buildings (structures) Conventional Energy Disaggregation Approach Conventional Smart Meters Corresponding Weather Condition Data Privacy Data Security Energy Consumption Fewer Data Processing Complexity High-frequency Data Hourly Smart Meter Data Knowledge Engineering Technique Learning in AI Machine Learning Based Disaggregation Machine Learning-based Pipeline Power Engineering Computing Smart Meters Statistics Weather Data
Content Type	Text
Resource Type	Article
Subject	Control and Systems Engineering Energy Engineering and Power Technology Electrical and Electronic Engineering

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1	Ministry of Education (GoI), Department of Higher Education	Sanctioning Authority	https://www.education.gov.in/ict-initiatives
2	Indian Institute of Technology Kharagpur	Host Institute of the Project: The host institute of the project is responsible for providing infrastructure support and hosting the project	https://www.iitkgp.ac.in
3	National Digital Library of India Office, Indian Institute of Technology Kharagpur	The administrative and infrastructural headquarters of the project	Dr. B. Sutradhar bsutra@ndl.gov.in
4	Project PI / Joint PI	Principal Investigator and Joint Principal Investigators of the project	Dr. B. Sutradhar bsutra@ndl.gov.in Prof. Saswat Chakrabarti will be added soon
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