scholarly journals An Enhanced Ensemble Approach for Non-Intrusive Energy Use Monitoring Based on Multidimensional Heterogeneity

Sensors ◽  
2021 ◽  
Vol 21 (22) ◽  
pp. 7750
Author(s):  
Yu Liu ◽  
Qianyun Shi ◽  
Yan Wang ◽  
Xin Zhao ◽  
Shan Gao ◽  
...  
Keyword(s):  
Energy Use ◽  
Low Voltage ◽  
Similarity Measures ◽  
Network Simulator ◽  
Ensemble Approach ◽  
Home Energy Management ◽  
Load Monitoring ◽  
Load Disaggregation ◽  
Combined Classifier ◽  
Technical Basis

Acting as a virtual sensor network for household appliance energy use monitoring, non-intrusive load monitoring is emerging as the technical basis for refined electricity analysis as well as home energy management. Aiming for robust and reliable monitoring, the ensemble approach has been expected in load disaggregation, but the obstacles of design difficulty and computational inefficiency still exist. To address this, an ensemble design integrated with multi-heterogeneity is proposed for non-intrusive energy use disaggregation in this paper. Firstly, the idea of utilizing a heterogeneous design is presented, and the corresponding ensemble framework for load disaggregation is established. Then, a sparse coding model is allocated for individual classifiers, and the combined classifier is diversified by introducing different distance and similarity measures without consideration of sparsity, forming mutually heterogeneous classifiers. Lastly, a multiple-evaluations-based decision process is fine-tuned following the interactions of multi-heterogeneous committees, and finally deployed as the decision maker. Through verifications on both a low-voltage network simulator and a field measurement dataset, the proposed approach is demonstrated to be effective in enhancing load disaggregation performance robustly. By appropriately introducing the heterogeneous design into the ensemble approach, load monitoring improvements are observed with reduced computational burden, which stimulates research enthusiasm in investigating valid ensemble strategies for practical non-intrusive load monitoring implementations.

scholarly journals Toward Robust Non-Intrusive Load Monitoring via Probability Model Framed Ensemble Method

Sensors ◽  
2021 ◽  
Vol 21 (21) ◽  
pp. 7272
Author(s):  
Yu Liu ◽  
Yan Wang ◽  
Yu Hong ◽  
Qianyun Shi ◽  
Shan Gao ◽  
...  
Keyword(s):  
Low Voltage ◽  
Probability Model ◽  
Ensemble Method ◽  
Classification Model ◽  
Network Simulator ◽  
Load Monitoring ◽  
Load Disaggregation ◽  
Individual Classifier ◽  
The Individual ◽  
High Flexibility

As a pivotal technological foundation for smart home implementation, non-intrusive load monitoring is emerging as a widely recognized and popular technology to replace the sensors or sockets networks for the detailed household appliance monitoring. In this paper, a probability model framed ensemble method is proposed for the target of robust appliance monitoring. Firstly, the non-intrusive load disaggregation-oriented ensemble architecture is presented. Then, dictionary learning model is utilized to formulate the individual classifier, while the sparse coding-based approach is capable of providing multiple solutions under greedy mechanism. Furthermore, a fully probabilistic model is established for combined classifier, where the candidate solutions are all labelled with probability scores and evaluated via two-stage decision-making. The proposed method is tested on both low-voltage network simulator platform and field measurement datasets, and the results show that the proposed ensemble method always guarantees an enhancement on the performance of non-intrusive load disaggregation. Besides, the proposed approach shows high flexibility and scalability in classification model selection. Therefore, by initializing the architecture and approach of ensemble method-based NILM, this work plays a pioneer role in using ensemble method to improve the robustness and reliability of non-intrusive appliance monitoring.

scholarly journals Feature Identification for Non-Intrusively Extracting Occupant Energy-Use Information in Office Buildings

2019 ◽  
Vol 1 (1) ◽  
Author(s):  
Hamed Nabizadeh Rafsanjani
Keyword(s):  
Capital Investment ◽  
Energy Use ◽  
Low Cost ◽  
Specific Energy Consumption ◽  
Cost Effective ◽  
Office Buildings ◽  
Specific Power ◽  
Feature Identification ◽  
Load Monitoring ◽  
Energy Consuming

Detailed energy-use information of office buildings’ occupants is necessary to implement proper simulation/intervention techniques. However, acquiring accurate occupant-specific energy consumption in office buildings at low cost is currently a challenging task since existing intrusive load monitoring (ILM) technologies require a large capital investment to provide high-resolution electricity usage data for individual occupants. On the other hand, non-intrusive load monitoring (NILM) approaches have been proven as more cost effective and flexible approaches to provide energy-use information of individual appliances. Therefore, extending the concept of NILM to individual occupants would be beneficial. This paper proposes two occupancy-related energy-consuming features, delay interval and magnitude of power changes and evaluates their significances for extracting occupant-specific power changes in a non-intrusive manner. The proposed features were examined through implementing a logistic regression model as a predictor on aggregate energy load data collected from an office building. Hypotheses tests also confirmed that both features are statistically significant to non-intrusively derive individual occupants’ energy-use information. As the main contribution of this study, these features could be utilized in developing sophisticated NILM-based approaches to monitor individual occupant energy-consuming behavior.  

scholarly journals ENERGY EFFICIENCY IN RESIDENTIAL BUILDINGS, LESS STRAIGHT FORWARD THEN PRESUMED

2018 ◽  
Vol 13 (1) ◽  
Author(s):  
Hugo Hens
Keyword(s):  
Energy Efficiency ◽  
Energy Efficient ◽  
Energy Use ◽  
Low Voltage ◽  
Residential Buildings ◽  
Primary Energy ◽  
Heating And Cooling ◽  
Net Zero ◽  
Energy Conserving ◽  
New Construction

Since the 1990s, the successive EU directives and related national or regional legislations require new construction and retrofits to be as much as possible energy-efficient. Several measures that should stepwise minimize the primary energy use for heating and cooling have become mandated as requirement. However, in reality, related predicted savings are not seen in practice. Two effects are responsible for that. The first one refers to dweller habits, which are more energy-conserving than the calculation tools presume. In fact, while in non-energy-efficient ones, habits on average result in up to a 50% lower end energy use for heating than predicted. That percentage drops to zero or it even turns negative in extremely energy-efficient residences. The second effect refers to problems with low-voltage distribution grids not designed to transport the peaks in electricity whensunny in summer. Through that, a part of converters has to be uncoupled now and then, which means less renewable electricity. This is illustrated by examples that in theory should be net-zero buildings due to the measures applied and the presence of enough photovoltaic cells (PV) on each roof. We can conclude that mandating extreme energy efficiency far beyond the present total optimum value for residential buildings looks questionable as a policy. However, despite that, governments and administrations still seem to require even more extreme measurements regarding energy efficiency.

scholarly journals Enhancing Non-intrusive Occupant Load Monitoring through Occupancy Matrix

2019 ◽  
Vol 2 (1) ◽  
Author(s):  
Hamed Nabizadeh Rafsanjani
Keyword(s):  
Energy Use ◽  
Cost Effective ◽  
Behavior Intervention ◽  
Built Environments ◽  
Load Variations ◽  
Office Space ◽  
Cost Effective Approach ◽  
Load Monitoring ◽  
Energy Consuming ◽  
The Moment

It has been universally accepted that energy consumption in commercial buildings is highly related to occupant behaviors. Improving occupants’ energy-use behaviors is regarded as the most cost-effective approach to enhance overall energy saving in commercial built environments. However, effective behavior intervention pursuits rely on the availability of occupant-specific energy-use information, which is extremely expensive to capture with existing technologies. In this context, the author’s previous studies proposed the non-intrusive occupant load monitoring (NIOLM) approach that captures individual occupants’ energy-consuming information at their entry and departure events in an economically feasible manner. The NIOLM assigns energy-load variations (ev) of a building to individual occupants and relies on two variables: Time delay intervals and magnitudes of ev. This paper extends the existing NIOLM concept with the inclusion of a new variable, the occupancy matrix which manifests the information of present occupants at the moment of ev. An experiment has been conducted in an office space to validate the feasibility and accuracy of the proposed approach. Outcomes of this research could be a great help for studies on occupant energy-use behaviors intervention and simulation. 

A load-disaggregation framework to sense personalized energy-use information in commercial buildings

2020 ◽  
Vol 207 ◽  
pp. 109633 ◽  
Author(s):  
Hamed Nabizadeh Rafsanjani ◽  
Sam Moayedi ◽  
Changbum R. Ahn ◽  
Mahmoud Alahmad
Keyword(s):  
Energy Use ◽  
Commercial Buildings ◽  
Load Disaggregation

scholarly journals MFRED, 10 second interval real and reactive power for groups of 390 US apartments of varying size and vintage

2020 ◽  
Vol 7 (1) ◽  
Author(s):  
Christoph J. Meinrenken ◽  
Noah Rauschkolb ◽  
Sanjmeet Abrol ◽  
Tuhin Chakrabarty ◽  
Victor C. Decalf ◽  
...  
Keyword(s):  
Energy Use ◽  
Reactive Power ◽  
Cooling System ◽  
High Time Resolution ◽  
Annual Average ◽  
Electricity Use ◽  
Load Monitoring ◽  
Residential Electricity ◽  
Data Points ◽  
Integration Of Renewables

Abstract Building electricity is a major component of global energy use and its environmental impacts. Detailed data on residential electricity use have many interrelated research applications, from energy conservation to non-intrusive load monitoring, energy storage, integration of renewables, and electric vs. fossil-based heating. The dataset presented here, Multifamily Residential Electricity Dataset (MFRED), contains the electricity use of 390 apartments, ranging from studios to four-bedroom units. All apartments are located in the Northeastern United States (IECC-climate-zone 4 A), but differ in their heating/cooling system and construction year (early to late 20th century). To adhere to privacy guidelines, data were averaged across 15 apartments each, based on annual electricity use. MFRED includes real and reactive power, at 10-second resolution, for January to December 2019 (246 million data points). The annual average real power per apartment is 343 W (3.27 W/m2 of floor area), with strong variation between seasons and apartment size. Considering its large number of apartments, high time resolution, real and reactive power, and 12-month duration, MFRED is currently unique for the multifamily-sector.

scholarly journals A Practical Load Disaggregation Approach for Monitoring Industrial Users Demand with Limited Data Availability

Energies ◽  
2021 ◽  
Vol 14 (16) ◽  
pp. 4880
Author(s):  
Sara Tavakoli ◽  
Kaveh Khalilpour
Keyword(s):  
Large Scale ◽  
Service Providers ◽  
Low Cost ◽  
Estimation Error ◽  
Data Availability ◽  
Smart Meters ◽  
Industrial Sectors ◽  
Load Monitoring ◽  
Load Disaggregation ◽  
Electrical Loads

The emergence of smart sensors has had a significant impact on the utility industry. In particular, it has made the planning and implementation of demand-side management (DSM) programmes easier. Nevertheless, for various reasons, some users may not implement smart meters for load monitoring. This paper addresses such cases, particularly large-scale industrial users, which, despite heavy electrical loads coming from many different processes, implement only simple energy measuring equipment for billing purposes. This necessitates the utilisation of novel methodologies for load disaggregation, often referred to as nonintrusive load monitoring (NILM). The availability of such tools can create multifold benefits for industrial park management, utility service providers, regulators, and policymakers. Here, we introduce an optimisation algorithm for nonintrusive load disaggregation that is low-cost, speedy, and acceptably accurate. As a case study, we used real network data of three industrial sectors: food processing, stonecutting, and glassmaking. For all cases, the optimisation framework developed a desegregated profile and estimated the load with an error of less than 5%. For non-workdays, given the higher uncertainty for the continuity of different processes, the estimation error was higher but still in an acceptable range of around 3.63–15.09% with an average of 8.10%.

scholarly journals Non-Intrusive Load Monitoring Based on Unsupervised Optimization Enhanced Neural Network Deep Learning

2021 ◽  
Vol 9 ◽  
Author(s):  
Yu Liu ◽  
Jiarui Wang ◽  
Jiewen Deng ◽  
Wenquan Sheng ◽  
Pengxiang Tan
Keyword(s):  
Neural Network ◽  
Learning Strategy ◽  
Learning Algorithm ◽  
Implementation Cost ◽  
Neural Network Learning ◽  
Model Following ◽  
The Neural Network ◽  
Load Monitoring ◽  
Public Dataset ◽  
Load Disaggregation

Non-intrusive load monitoring has broad application prospects because of its low implementation cost and little interference to energy users, which has been highly expected in the industrial field recently due to the development of learning algorithms. Targeting at the investigation of practical and reliable load monitoring in field implementations, a non-intrusive load disaggregation approach based on an enhanced neural network learning algorithm is proposed in this article. The presented appliance monitoring approach establishes the neural network model following the supervised learning strategy at first and then utilizes the unsupervised learning based optimization to enhance the flexibility and adaptability for diverse scenarios, leading to the improvement of disaggregation performance. By verifications on the REDD public dataset, the proposed approach is demonstrated to be with good performance in non-intrusive load monitoring. In addition to the accuracy enhancement, the proposed approach is also with good scalability, which is efficient in recognizing the newly added appliance.

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