{"id":{"repo_id":"uoit","oai_identifier":"oai:ontariotechu.scholaris.ca:10155/735"},"canonical_url":"https://search.dev.ndltd.org/etd/uoit/oai:ontariotechu.scholaris.ca:10155/735","repository":{"repo_id":"uoit","name":"Ontario Institute of Technology","base_url":"https://ontariotechu.scholaris.ca/server/oai/request"},"display":{"title":"Machine learning classification techniques for non-intrusive load monitoring","abstract":"Non-intrusive load monitoring is the concept of determining the operational loads using single-point sensing. The features contained within the electrical load’s signal are used to identify a unique signature which is used by a machine learning classifier to automate the load identification process. In this thesis, existing machine learning classification techniques are reviewed within the context of the non-intrusive load monitoring application. A non-intrusive load monitoring algorithm is developed in this to extract the prominent hidden features contained within the electrical load’s signal which helps identify the operation of different appliances from a single point of an electrical circuit. Decision tree and Naïve Bayes classifiers are used as the machine learning classification technique to automate the load classification process. The co-testing of machine learning classifiers was introduced in this work to improve the classification accuracy of previously seen methods when applying the one-against-the-rest testing approach. When the proposed NILM algorithm was applied to a real test system, a classification accuracy of 99.61% for decision tree and 99.38% for Naïve Bayes was obtained. When compared to previous methods in literature utilizing one-against-the-rest testing approach, a classification accuracy of 76.31% for decision tree and 67.44% for Naïve Bayes was obtained. The results demonstrate the effectiveness of the proposed non-intrusive load monitoring approach through the notable significant increase in the observed classification accuracies.","abstract_html":"Non-intrusive load monitoring is the concept of determining the operational loads using single-point sensing. The features contained within the electrical load’s signal are used to identify a unique signature which is used by a machine learning classifier to automate the load identification process. In this thesis, existing machine learning classification techniques are reviewed within the context of the non-intrusive load monitoring application. A non-intrusive load monitoring algorithm is developed in this to extract the prominent hidden features contained within the electrical load’s signal which helps identify the operation of different appliances from a single point of an electrical circuit. Decision tree and Naïve Bayes classifiers are used as the machine learning classification technique to automate the load classification process. The co-testing of machine learning classifiers was introduced in this work to improve the classification accuracy of previously seen methods when applying the one-against-the-rest testing approach. When the proposed NILM algorithm was applied to a real test system, a classification accuracy of 99.61% for decision tree and 99.38% for Naïve Bayes was obtained. When compared to previous methods in literature utilizing one-against-the-rest testing approach, a classification accuracy of 76.31% for decision tree and 67.44% for Naïve Bayes was obtained. The results demonstrate the effectiveness of the proposed non-intrusive load monitoring approach through the notable significant increase in the observed classification accuracies.","abstract_has_math":false,"creators":["Chung, Jefferson"],"institution":"University of Ontario Institute of Technology","degree_name":"Master of Applied Science (MASc)","degree_level":null,"degree_discipline":"Electrical and Computer Engineering","degree_department":null,"school":null,"contributors":[],"advisors":["Ibrahim, Walid Morsi"],"committee_chairs":[],"committee_members":[],"year":2016,"date_issued":"2016-10-01","date_published":"2016-10-01","updated_at":"2026-07-24T05:35:16Z","subjects":["Machine learning","Non-intrusive load monitoring","Co-testing"],"languages":["en"],"rights":[],"rights_urls":[],"identifier_entries":[]},"links":{"outbound_url":"https://hdl.handle.net/10155/735","outbound_label":"Handle","outbound_source":"dc:identifier.uri"},"metadata_groups":[{"id":"people","label":"People","entries":[{"key":"dc:contributor.advisor","label":"Advisor","values":["Ibrahim, Walid Morsi"]},{"key":"dc:creator","label":"Author","values":["Chung, Jefferson"]}]},{"id":"academic_context","label":"Academic Context","entries":[{"key":"dc:date.accessioned","label":"Dc Date Accessioned","values":["2017-04-20T19:08:11Z","2022-03-29T16:41:17Z"]},{"key":"dc:date.available","label":"Dc Date Available","values":["2017-04-20T19:08:11Z","2022-03-29T16:41:17Z"]},{"key":"dc:date.issued","label":"Date","values":["2016-10-01"]},{"key":"dc:type","label":"Dc Type","values":["Thesis"]},{"key":"thesis:degree_discipline","label":"Discipline","values":["Electrical and Computer Engineering"]},{"key":"thesis:degree_name","label":"Degree Name","values":["Master of Applied Science (MASc)"]},{"key":"thesis:institution_name","label":"Thesis Institution Name","values":["University of Ontario Institute of Technology"]}]},{"id":"subjects_keywords","label":"Subjects and Keywords","entries":[{"key":"dc:subject","label":"Dc Subject","values":["Machine learning","Non-intrusive load monitoring","Co-testing"]}]},{"id":"language_rights","label":"Language and Rights","entries":[{"key":"dc:language.iso","label":"Language (ISO)","values":["en"]}]},{"id":"identifiers","label":"Identifiers","entries":[{"key":"dc:identifier.uri","label":"Identifier URI","values":["https://hdl.handle.net/10155/735"]}]},{"id":"additional","label":"Additional Metadata","entries":[{"key":"dc:description.abstract","label":"Abstract","values":["Non-intrusive load monitoring is the concept of determining the operational loads using single-point sensing. The features contained within the electrical load’s signal are used to identify a unique signature which is used by a machine learning classifier to automate the load identification process. In this thesis, existing machine learning classification techniques are reviewed within the context of the non-intrusive load monitoring application. A non-intrusive load monitoring algorithm is developed in this to extract the prominent hidden features contained within the electrical load’s signal which helps identify the operation of different appliances from a single point of an electrical circuit. Decision tree and Naïve Bayes classifiers are used as the machine learning classification technique to automate the load classification process. The co-testing of machine learning classifiers was introduced in this work to improve the classification accuracy of previously seen methods when applying the one-against-the-rest testing approach. When the proposed NILM algorithm was applied to a real test system, a classification accuracy of 99.61% for decision tree and 99.38% for Naïve Bayes was obtained. When compared to previous methods in literature utilizing one-against-the-rest testing approach, a classification accuracy of 76.31% for decision tree and 67.44% for Naïve Bayes was obtained. The results demonstrate the effectiveness of the proposed non-intrusive load monitoring approach through the notable significant increase in the observed classification accuracies."]},{"key":"dc:title","label":"Title","values":["Machine learning classification techniques for non-intrusive load monitoring"]}]}],"canonical_facts":{"dc:contributor.advisor":["Ibrahim, Walid Morsi"],"dc:creator":["Chung, Jefferson"],"dc:date.accessioned":["2017-04-20T19:08:11Z","2022-03-29T16:41:17Z"],"dc:date.available":["2017-04-20T19:08:11Z","2022-03-29T16:41:17Z"],"dc:date.issued":["2016-10-01"],"dc:description.abstract":["Non-intrusive load monitoring is the concept of determining the operational loads using single-point sensing. The features contained within the electrical load’s signal are used to identify a unique signature which is used by a machine learning classifier to automate the load identification process. In this thesis, existing machine learning classification techniques are reviewed within the context of the non-intrusive load monitoring application. A non-intrusive load monitoring algorithm is developed in this to extract the prominent hidden features contained within the electrical load’s signal which helps identify the operation of different appliances from a single point of an electrical circuit. Decision tree and Naïve Bayes classifiers are used as the machine learning classification technique to automate the load classification process. The co-testing of machine learning classifiers was introduced in this work to improve the classification accuracy of previously seen methods when applying the one-against-the-rest testing approach. When the proposed NILM algorithm was applied to a real test system, a classification accuracy of 99.61% for decision tree and 99.38% for Naïve Bayes was obtained. When compared to previous methods in literature utilizing one-against-the-rest testing approach, a classification accuracy of 76.31% for decision tree and 67.44% for Naïve Bayes was obtained. The results demonstrate the effectiveness of the proposed non-intrusive load monitoring approach through the notable significant increase in the observed classification accuracies."],"dc:identifier.uri":["https://hdl.handle.net/10155/735"],"dc:language.iso":["en"],"dc:subject":["Machine learning","Non-intrusive load monitoring","Co-testing"],"dc:title":["Machine learning classification techniques for non-intrusive load monitoring"],"dc:type":["Thesis"],"thesis:degree_discipline":["Electrical and Computer Engineering"],"thesis:degree_name":["Master of Applied Science (MASc)"],"thesis:institution_name":["University of Ontario Institute of Technology"]},"updated_at":"2026-07-24T05:35:16Z"}