scholarly journals A Fusion Load Disaggregation Method Based on Clustering Algorithm and Support Vector Regression Optimization for Low Sampling Data

2019 ◽  
Vol 11 (2) ◽  
pp. 51 ◽  
Author(s):  
Quanbo Yuan ◽  
Huijuan Wang ◽  
Botao Wu ◽  
Yaodong Song ◽  
Hejia Wang
Keyword(s):  
Energy Consumption ◽  
Support Vector Regression ◽  
Clustering Algorithm ◽  
Low Frequency ◽  
Total Power ◽  
Support Vector ◽  
Smart Meters ◽  
Load Monitoring ◽  
Load Disaggregation ◽  
Economic Technology

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), a novel and economic technology, obtains single-appliance power consumption through a single total power meter. This paper, focusing on load disaggregation with low hardware costs, proposed a load disaggregation method for low sampling data from smart meters based on a clustering algorithm and support vector regression optimization. This approach combines the k-median algorithm and dynamic time warping to identify the operating appliance and retrieves single energy consumption from an aggregate smart meter signal via optimized support vector regression (OSVR). Experiments showed that the technique can recognize multiple devices switching on at the same time using low-frequency data and achieve a high load disaggregation performance. The proposed method employs low sampling data acquired by smart meters without installing extra measurement equipment, which lowers hardware cost and is suitable for applications in smart grid environments.

scholarly journals Event Matching Classification Method for Non-Intrusive Load Monitoring

2021 ◽  
Vol 13 (2) ◽  
pp. 693
Author(s):  
Elnaz Azizi ◽  
Mohammad T. H. Beheshti ◽  
Sadegh Bolouki
Keyword(s):  
Energy Management ◽  
Low Frequency ◽  
Training Dataset ◽  
Mode Transition ◽  
Smart Meters ◽  
Available Energy ◽  
Consumption Values ◽  
Load Monitoring ◽  
Transition Times ◽  
Event Based

Nowadays, energy management aims to propose different strategies to utilize available energy resources, resulting in sustainability of energy systems and development of smart sustainable cities. As an effective approach toward energy management, non-intrusive load monitoring (NILM), aims to infer the power profiles of appliances from the aggregated power signal via purely analytical methods. Existing NILM methods are susceptible to various issues such as the noise and transient spikes of the power signal, overshoots at the mode transition times, close consumption values by different appliances, and unavailability of a large training dataset. This paper proposes a novel event-based NILM classification algorithm mitigating these issues. The proposed algorithm (i) filters power signals and accurately detects all events; (ii) extracts specific features of appliances, such as operation modes and their respective power intervals, from their power signals in the training dataset; and (iii) labels with high accuracy each detected event of the aggregated signal with an appliance mode transition. The algorithm is validated using REDD with the results showing its effectiveness to accurately disaggregate low-frequency measured data by existing smart meters.

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%.

Energy-aware VM migration using dragonfly–crow optimization and support vector regression model in Cloud

2018 ◽  
Vol 09 (06) ◽  
pp. 1850050 ◽  
Author(s):  
Nitin S. More ◽  
Rajesh B. Ingle
Keyword(s):  
Energy Consumption ◽  
Support Vector Regression ◽  
Optimization Algorithm ◽  
Search Algorithm ◽  
Support Vector ◽  
Load Prediction ◽  
Energy Aware ◽  
Support Vector Regression Model ◽  
Migration Cost ◽  
And Migration

Nowadays, virtual machine migration (VMM) is a trending research since it helps in balancing the load of the Cloud effectively. Several VMM-based strategies defined in the literature have considered various metrics, such as load, energy, and migration cost for balancing the load of the model. This paper introduces a novel VMM strategy by considering the load of the Cloud network. Two important aspects of the proposed scheme are the load prediction through the support vector regression (SVR) and the optimal VM placement through the proposed dragonfly-based crow (D-Crow) optimization algorithm. The proposed D-Crow optimization algorithm is developed by incorporating crow search algorithm (CSA) into dragonfly algorithm (DA). Also, the proposed VMM strategy defines a load balancing model based on the energy consumption, load, and the migration cost to achieve the energy-aware VMM. The simulation of the proposed VMM strategy is done based on the metrics such as load, energy consumption, and the migration cost. From the results, it can be shown that the proposed VMM strategy surpassed other comparative models by achieving the minimum values of 7.3719%, 10.0368%, and 11.0639% for the load, energy consumption, and migration cost, respectively.

scholarly journals Online Ash Fouling Prediction for Boiler Heating Surfaces based on Wavelet Analysis and Support Vector Regression

Energies ◽  
2019 ◽  
Vol 13 (1) ◽  
pp. 59 ◽  
Author(s):  
Shuiguang Tong ◽  
Xiang Zhang ◽  
Zheming Tong ◽  
Yanling Wu ◽  
Ning Tang ◽  
...  
Keyword(s):  
Thermal Resistance ◽  
Support Vector Regression ◽  
Online Monitoring ◽  
Low Frequency ◽  
Fixed Time ◽  
Operating Conditions ◽  
Support Vector ◽  
Ash Layer ◽  
Heating Surfaces ◽  
Ash Fouling

Depending on its operating conditions, traditional soot blowing is activated for a fixed time. However, low-frequency soot blowing can cause heat transfer efficiency to decrease. High-frequency soot blowing not only wastes high-pressure steam, but also abrades surface pipes, reducing the working life of a heat exchange device. Therefore, it is necessary to design an online ash fouling monitoring system to perform soot blowing that is dependent on the status of ash accumulation. This study presents an online monitoring model of ash-layer thermal resistance that reflects the degree of ash fouling. A wavelet threshold denoising algorithm was applied to smooth the thermal resistance of the ash layer calculated by the heat balance mechanism model. Thus, the variation in thermal resistance becomes more visible, which is more conducive to optimizing the operation of soot blowing. The designed Support Vector Regression (SVR) model could achieve the online prediction of thermal resistance denoising for low-temperature superheaters. Experimental analysis indicates that the prediction accuracy was 98.5% during the testing phase. By using the method proposed in this study, online monitoring of heating surfaces during the ash fouling process can be realized without adding complicated and expensive equipment.

scholarly journals Forecasting the Heat Load of Residential Buildings with Heat Metering Based on CEEMDAN-SVR

Energies ◽  
2020 ◽  
Vol 13 (22) ◽  
pp. 6079
Author(s):  
Xiaoyu Gao ◽  
Chengying Qi ◽  
Guixiang Xue ◽  
Jiancai Song ◽  
Yahui Zhang ◽  
...  
Keyword(s):  
Energy Consumption ◽  
Prediction Model ◽  
Support Vector Regression ◽  
Heat Load ◽  
Residential Buildings ◽  
Ensemble Prediction ◽  
Support Vector ◽  
District Heating System ◽  
Gradual Improvement ◽  
Heat Metering

The energy demand of the district heating system (DHS) occupies an important part in urban energy consumption, which has a great impact on the energy security and environmental protection of a city. With the gradual improvement of people’s economic conditions, different groups of people now have different demands for thermal energy for their comfort. Hence, heat metering has emerged as an imperative for billing purposes and sustainable management of energy consumption. Therefore, forecasting the heat load of buildings with heat metering on the demand side is an important management strategy for DHSs to meet end-users’ needs and maintain energy-saving regulations and safe operation. However, the non-linear and non-stationary characteristics of buildings’ heat load make it difficult to predict consumption patterns accurately, thereby limiting the capacity of the DHS to deliver on its statutory functions satisfactorily. A novel ensemble prediction model is proposed to resolve this problem, which integrates the advantages of Complete Ensemble Empirical Mode Decomposition with Adaptive Noise (CEEMDAN) and support vector regression (SVR), called CEEMDAN-SVR in this paper. The proposed CEEMDAN-SVR algorithm is designed to automatically decompose the intrinsic mode according to the characteristics of heat load data to ensure an accurate representation of heat load patterns on multiple time scales. It will also be useful for developing an accurate prediction model for the buildings’ heat load. In formulating the CEEMDAN-SVR model, the heat load data of three different buildings in Xingtai City were acquired during the heating season of 2019–2020 and employed to conduct detailed comparative analysis with modern algorithms, such as extreme tree regression (ETR), forest tree regression (FTR), gradient boosting regression (GBR), support vector regression (SVR, with linear, poly, radial basis function (RBF) kernel), multi-layer perception (MLP) and EMD-SVR. Experimental results reveal that the performance of the proposed CEEMDAN-SVR model is better than the existing modern algorithms and it is, therefore, more suitable for modeling heat load forecasting.

Sign in / Sign up

Export Citation Format

Share Document