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An Effective Non-Intrusive Load Monitoring (NILM) for Residential Appliances using Wavelet Transform and Clustering
| International Journal of Computer Applications |
| Foundation of Computer Science (FCS), NY, USA |
| Volume 186 - Number 32 |
| Year of Publication: 2024 |
| Authors: Nobert Kyereboah Mensah, Hamidu Abdel-Fatao, Yao Yevenyo Ziggah, Solomon Nunoo |
10.5120/ijca2024923901
|
Nobert Kyereboah Mensah, Hamidu Abdel-Fatao, Yao Yevenyo Ziggah, Solomon Nunoo . An Effective Non-Intrusive Load Monitoring (NILM) for Residential Appliances using Wavelet Transform and Clustering. International Journal of Computer Applications. 186, 32 ( Aug 2024), 25-40. DOI=10.5120/ijca2024923901
Abstract
In recent years, energy consumption has gained attention due to the impact of human activities on the environment as well as the economic burden imposed by energy consumption on end users. Understanding how energy is consumed at the appliance level is crucial yet challenging with conventional aggregate energy data from utility companies. Non-Intrusive Load Monitoring (NILM) emerges as a solution, enabling the estimation of individual appliance energy usage from aggregate data. This study proposes an innovative approach to cluster residential appliances within NILM systems, aiming to identify appliance usage patterns and facilitate predictive modeling using only aggregate consumption data. Adopting wavelet transformation for data denoising, coupled with K-Means and Self-Organising Map (SOM) clustering algorithms, the methodology identifies appliances with similar consumption patterns. A comparative evaluation is performed to assess the performance of these clustering methods, considering aspects such as accuracy, computational time, and interpretability of results. To validate the proposed methodology, real-world data from the REFIT dataset, offering detailed information on the energy consumption of individual household appliances, is employed. The study not only aids in the selection of clustering algorithms for NILM but also provides insights into the strengths and limitations of each algorithm in the context of energy management within smart grid systems.
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