In this article an energy disaggregation architecture using elastic matching algorithms is presented. The architecture uses a database of reference energy consumption signatures and compares them with incoming energy consumption frames using template...
Energy-saving schemes are nowadays a major worldwide concern. As the building sector is a major energy consumer, and hence greenhouse gas emitter, research in home energy management systems (HEMS) has increased substantially during the last years. On...
Energy disaggregation (ED), with minimal infrastructure, can create energy awareness and thus promote energy efficiency by providing appliance-level consumption information. However, ED is highly ill-posed and gets complicated with increase in number...
Recent research on non-intrusive load monitoring, or load disaggregation, suggests that the performance of algorithms can be affected by factors beyond energy data. In particular, by incorporating non-electric data in load disaggregation analysis, su...
Non-intrusive load monitoring (NILM) enables the disaggregation of appliance-level energy consumption from aggregate electrical signals, offering a scalable solution for improving efficiency. This study compared the performance of traditional NILM al...
Recently, optimization-based energy disaggregation (ED) algorithms have been gaining significance due to their capability to perform disaggregation with minimal information compared to the pattern-based ED algorithms, which demand large amounts of da...
Agricultural policies have impacts on land use, the economy, and the environment and their analysis requires disaggregated data at the local level with geographical references. Thus, this study proposes a model for disaggregating agricultural data, w...
Water monitoring in households is important to ensure the sustainability of fresh water reserves on our planet. It provides stakeholders with the statistics required to formulate optimal strategies in residential water management. However, this shoul...
Appliance Load Monitoring (ALM) is essential for energy management solutions, allowing them to obtain appliance-specific energy consumption statistics that can further be used to devise load scheduling strategies for optimal energy utilization. Fine-...
In recent years, Smart Grids have been developing globally. Since smart meters only acquire low-frequency data, non-intrusive load monitoring technology using the signature extracted from high-frequency data needs an additional measurement device to...
A non-intrusive load monitoring (NILM) process is intended to allow for the separation of individual appliances from an aggregated energy reading in order to estimate the operation of individual loads. In the past, electricity meters specified only a...
Scaling models is one of the challenges for water resource planning and management, with the aim of bringing the developed models into practice by applying them to predict water quality and quantity for catchments that lack sufficient data. For this...
Nonintrusive appliance load monitoring (NIALM) allows disaggregation of total electricity consumption into particular appliances in domestic or industrial environments. NIALM systems operation is based on processing of electrical signals acquired at...
Energy disaggregation algorithms disintegrate aggregate demand into appliance-level demands. Among various energy disaggregation approaches, non-intrusive load monitoring (NILM) algorithms requiring a single sensor have gained much attention in recen...
Accurate time series forecasting often requires higher temporal resolution than that provided by available data, such as when daily forecasts are needed from monthly data. Existing temporal disaggregation techniques, which typically handle only singl...
This paper introduces the first completely unsupervised methodology for non-intrusive load monitoring that does not rely on any additional data, making it suitable for real-life applications. The methodology includes an algorithm to efficiently decom...
In order to achieve more efficient energy consumption, it is crucial that accurate detailed information is given on how power is consumed. Electricity details benefit both market utilities and also power consumers. Non-intrusive load monitoring (NILM...
In an electrical safe microenvironment, all kinds of electrical appliances can be operated safely to ensure the safety of life and property. The significance of safety hazard factors detection is to detect safety hazards in advance, to remind the adm...
Obtaining reliable and detailed energy consumption information about building service (BS) systems is an essential prerequisite for identifying energy-saving potential and improving energy efficiency of a building. Therefore, in recent years, energy...
Two multicriteria-sorting methods that generalize the relational paradigm have been recently presented in the literature. One uses objects representative of classes, the other uses objects in the limiting boundaries of classes; both can use either a...
The increasing need for efficient energy consumption monitoring, driven by economic and environmental concerns, has made Non-Intrusive Load Monitoring (NILM) a cost-effective alternative to traditional measurement methods. Despite its progress since...
For many years, energy monitoring at the most disaggregate level has been mainly sought through the idea of Non-Intrusive Load Monitoring (NILM). Developing a practical application of this concept in the residential sector can be impeded by the techn...
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 d...
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 V...
A smart grid is a promising use-case of AIoT (AI (artificial intelligence) across IoT (internet of things)) that enables bidirectional communication among utilities that arises with demand response (DR) schemes for demand-side management (DSM) and co...
Global soil moisture (SM) products are currently available from passive microwave sensors at typically 40 km spatial resolution. Although recent efforts have been made to produce 1 km resolution data from the disaggregation of coarse scale observatio...
Load disaggregation has emerged as an effective tool for enabling smarter energy management in residential and commercial buildings. By providing appliance-level energy consumption estimation from aggregate data, it supports energy efficiency initiat...
The objective of this study is to develop artificial neural network (ANN) models, including multilayer perceptron (MLP) and Kohonen self-organizing feature map (KSOFM), for spatial disaggregation of areal rainfall in the Wi-stream catchment, an Inte...
Thermal data products derived from remotely sensed data play significant roles as key parameters for biophysical phenomena. However, a trade-off between spatial and spectral resolutions has existed in thermal infrared (TIR) remote sensing systems, wi...
Nonintrusive load monitoring (NILM) is a process that disaggregates individual energy consumption based on the total energy consumption. In this study, an energy disaggregation model was developed and verified using an algorithm based on a recurrent...
Non-intrusive load monitoring (NILM) is an approach that helps residents obtain detailed information about household electricity consumption and has gradually become a research focus in recent years. Most of the existing algorithms on NILM build ener...
Demand side management has a vital role in supporting the demand response in smart grid infrastructure, in the decision-making of energy management, in household applications is significantly affected by the load-forecasting accuracy. This paper intr...
The estimation of electric power utilization, its baseload, and its heating, light, ventilation, and air-conditioning (HVAC) power component, which represents a very large portion of electricity usage in commercial facilities, are important for energ...
Non-intrusive load monitoring (NILM) is a process of estimating operational states and power consumption of individual appliances, which if implemented in real-time, can provide actionable feedback in terms of energy usage and personalized recommenda...
Disaggregating residential water end use events through the available commercial tools needs a great investment in time to manually process smart metering data. Therefore, it is extremely difficult to achieve a homogenous and sufficiently large corpu...
Non-intrusive load monitoring (NILM) is the task of disaggregating the total power consumption into its individual sub-components. Over the years, signal processing and machine learning algorithms have been combined to achieve this. Many publications...
Non-intrusive load disaggregation is a technique that monitors the total electrical load of an entire building or household. It uses a single power metering device to measure the total load. Then, it employs algorithms to break it down into the indiv...
In this paper, we address whether using a disaggregated series or combining an aggregated and disaggregated series improves the forecasting of the aggregated series compared to using the aggregated series alone. We used econometric techniques, such a...
Owing to low temporal resolution and cloud interference, there is a shortage of high spatial resolution remote sensing data. To address this problem, this study introduces a modified spatial and temporal data fusion approach (MSTDFA) to generate dail...
Non-intrusive load monitoring (NILM) is an effective method to optimize energy consumption patterns. Since the concept of NILM was proposed, extensive research has focused on energy disaggregation or load identification. The traditional method is to...
Load disaggregation determines appliance-level energy consumption unintrusively from aggregated consumption measured by a single meter. Deep neural networks have been proven to have great potential in load disaggregation. In this article, a temporal...
Demand response services and energy communities are set to be vital in bringing citizens to the core of the energy transition. The success of load flexibility integration in the electricity market, provided by demand response services, will depend on...
The trade-off between spatial and temporal resolutions has led to the disaggregation of remotely sensed land surface temperatures (LSTs) for better applications. The window used for regression is one of the primary factors affecting the disaggregatio...
Non-intrusive load monitoring (NILM), monitoring single-appliance consumption level by decomposing the aggregated energy consumption, is a novel and economic technology that is beneficial to energy utilities and energy demand management strategies de...
In this study, we deal with the application of multiangular data from the Multiangle Imaging Spectroradiometer (MISR) sensor for studying the effect of surface anisotropy and directional information on the classification accuracy for different land c...
Non-intrusive load monitoring (NILM) is a core technology for demand response (DR) and energy conservation services. Traditional NILM methods are rarely combined with practical applications, and most studies aim to disaggregate the whole loads in a h...
Non-intrusive load monitoring (NILM) has emerged as a pivotal technology in energy management applications by enabling precise monitoring of individual appliance energy consumption without the requirements of intrusive sensors or smart meters. In thi...
Estimating household energy use patterns and user consumption habits is a fundamental requirement for management and control techniques of demand response programs, leading to a growing interest in non-intrusive load disaggregation methods. In this w...
Non-intrusive load monitoring (NILM) is the main method used to monitor the energy footprint of a residential building and disaggregate total electrical usage into appliance-related signals. The most common disaggregation algorithms are based on the...
Energy behaviours will play a key role in decarbonising the building sector but require the provision of tailored insights to assist occupants to reduce their energy use. Energy disaggregation has been proposed to provide such information on the appl...
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