Results 21 to 30 of about 57,754 (187)
Iterative signal separation assisted energy disaggregation [PDF]
2015 Sixth International Green and Sustainable Computing Conference (IGSC), 2015 © 2016 IEEE; 2015 Sixth International Green and Sustainable Computing Conference (IGSC)
Pathak, Nilavra +2 more
openaire +1 more source
Energy Disaggregation via Deep Temporal Dictionary Learning [PDF]
IEEE Transactions on Neural Networks and Learning Systems, 2020 This paper addresses the energy disaggregation problem, i.e. decomposing the electricity signal of a whole home to its operating devices. First, we cast the problem as a dictionary learning (DL) problem where the key electricity patterns representing consumption behaviors are extracted for each device and stored in a dictionary matrix.
Mahdi Khodayar +2 more
openaire +3 more sources
Efficient Supervised Machine Learning Network for Non-Intrusive Load Monitoring
Technologies, 2022 From a single meter that measures the entire home’s electrical demand, energy disaggregation calculates appliance-by-appliance electricity consumption.
Muhammad Usman Hadi +2 more
doaj +1 more source
A NILM method for cooling load disaggregation based on artificial neural network [PDF]
E3S Web of Conferences, 2019 The demand of building energy management has increased due to high energy saving potentials. Load monitor and disaggregation can provide useful information for building energy management systems with detailed and individual loads of the building, so ...
Xiao Ziwei +4 more
doaj +1 more source
Non-Intrusive Load Disaggregation Based on a Multi-Scale Attention Residual Network
Applied Sciences, 2020 Non-intrusive load disaggregation (NILD) is of great significance to the development of smart grids. Current energy disaggregation methods extract features from sequences, and this process easily leads to a loss of load features and difficulties in ...
Liguo Weng +5 more
doaj +1 more source
Statistical and Electrical Features Evaluation for Electrical Appliances Energy Disaggregation [PDF]
, 2019 In this paper we evaluate several well-known and widely used machine learning algorithms for regression in the energy disaggregation task. Specifically, the Non-Intrusive Load Monitoring approach was considered and the K-Nearest-Neighbours, Support ...
Harell +3 more
core +2 more sources
NILM-based approach for energy efficiency assessment of household appliances
Energy Informatics, 2020 This paper presents a novel Non-Intrusive Load Monitoring (NILM) approach focusing on the Energy Efficiency (EE) assessment of residential appliances.
Fernando D. Garcia +3 more
doaj +1 more source
A Novel Denoising Auto-Encoder-Based Approach for Non-Intrusive Residential Load Monitoring
Energies, 2022 Mounting concerns pertaining to energy efficiency have led to the research of load monitoring. By Non-Intrusive Load Monitoring (NILM), detailed information regarding the electric energy consumed by each appliance per day or per hour can be formed.
Xi He, Heng Dong, Wanli Yang, Jun Hong
doaj +1 more source
Energy Reports, 2021 Toward the smart power and energy consumption, non-intrusive load monitoring is emerging as the promising technical assistance of intelligent energy user.
Yu Liu +4 more
doaj +1 more source
Prior Day Effect in Forecasting Daily Natural Gas Flow from Monthly Data [PDF]
, 2018 Many needs exist in the energy industry where measurement is monthly yet daily values are required. The process of disaggregation of low frequency measurement to higher frequency values has been presented in this literature.
Brown, Ronald H. +2 more
core +2 more sources