scholarly journals Partitional Clustering-Hybridized Neuro-Fuzzy Classification Evolved through Parallel Evolutionary Computing and Applied to Energy Decomposition for Demand-Side Management in a Smart Home

Processes ◽  
2021 ◽  
Vol 9 (9) ◽  
pp. 1539
Author(s):  
Yu-Chen Hu ◽  
Yu-Hsiu Lin ◽  
Harinahalli Lokesh Gururaj
Keyword(s):  
Energy Consumption ◽  
Electric Energy ◽  
Smart Meter ◽  
Energy Decomposition ◽  
Smart Meters ◽  
Electrical Grid ◽  
Electric Energy Consumption ◽  
Partitional Clustering ◽  
Neuro Fuzzy ◽  
Consumption Data

The key advantage of smart meters over rotating-disc meters is their ability to transmit electric energy consumption data to power utilities’ remote data centers. Besides enabling the automated collection of consumers’ electric energy consumption data for billing purposes, data gathered by smart meters and analyzed through Artificial Intelligence (AI) make the realization of consumer-centric use cases possible. A smart meter installed in a domestic sector of an electrical grid and used for the realization of consumer-centric use cases is located at the entry point of a household/building’s electrical grid connection and can gather composite/circuit-level electric energy consumption data. However, it is not able to decompose its measured circuit-level electric energy consumption into appliance-level electric energy consumption. In this research, we present an AI model, a neuro-fuzzy classifier integrated with partitional clustering and metaheuristically optimized through parallel-computing-accelerated evolutionary computing, that performs energy decomposition on smart meter data in residential demand-side management, where a publicly available UK-DALE (UK Domestic Appliance-Level Electricity) dataset is used to experimentally test the presented model to classify the On/Off status of monitored electrical appliances. As shown in this research, the presented AI model is effective at providing energy decomposition for domestic consumers. Further, energy decomposition can be provided for industrial as well as commercial consumers.

Willingness of Electricity Consumer in Malaysia to Share Electric Energy Consumption Data

2021 ◽  
pp. 689-700
Author(s):  
Salman Yussof ◽  
Nurul Nazeera Mohd Zulkefle ◽  
Yunus Yusoff ◽  
Asmidar Abu Bakar
Keyword(s):  
Energy Consumption ◽  
Electric Energy ◽  
Electric Energy Consumption ◽  
Consumption Data

Analysis and Design of Electric Energy Consumption Inspection Equipment Based on ARM9

2012 ◽  
Vol 157-158 ◽  
pp. 447-451
Author(s):  
Hu Hu ◽  
Xin Tian ◽  
Li Hong Han ◽  
Bin Chen
Keyword(s):  
Energy Consumption ◽  
Real Time ◽  
Low Cost ◽  
Electric Energy ◽  
Basic Structure ◽  
Analysis And Design ◽  
Electric Energy Consumption ◽  
Real Time Analysis ◽  
Consumption Data ◽  
Real Time Transmission

The present paper introduces a sort of analysis and design of electric energy consumption inspection equipment based on ARM9, which can inspect multiple electric energy indexes and conduct a real time inspection to electric energy consumption. Both a real time collection and a real time transmission of electric energy consumption data are realized and a real time analysis of these data that are transmitted through the network to the host computer can be carried out as well, the features of which are low power consumption, low cost, very applicable, high real time performance, etc. The paper also describes the system’s basic structure, hardware design, software design and system debugging process.

Agent-Based Simulation of Electric Energy Consumers

2014 ◽  
pp. 239-253 ◽  
Author(s):  
Fernanda Mota ◽  
Iverton Santos ◽  
Graçaliz Dimuro ◽  
Vagner Rosa ◽  
Silvia Botelho
Keyword(s):  
Energy Consumption ◽  
Multiagent Systems ◽  
Winter Season ◽  
Electric Energy ◽  
Synthetic Data ◽  
Agent Based ◽  
Electric Energy Consumption ◽  
Consumption Data ◽  
The Individual ◽  
Residential Consumption

The electric energy consumption is one of the main indicators of both the economic development and the quality of life of a society. However, the electric energy consumption data of individual home use is hard to obtain due to several reasons, such as privacy issues. In this sense, the social simulation based on multiagent systems comes as a promising option to deal with this difficulty through the production of synthetic electric energy consumption data. In a multiagent system the intelligent global behavior can be achieved from the behavior of the individual agents and their interactions. This chapter proposes a tool for simulation of electric energy consumers, based on multiagent systems concepts using the NetLogo tool. The tool simulates the residential consumption during working days and presented as a result the synthetic data average monthly consumption of residences, which varies according to income. So, the analysis of the produced simulation results show that economic consumers of the income 1 in the summer season had the lowest consumption among all other consumers and consumers noneconomic income 6 in the winter season had the highest.

scholarly journals Bagging Ensemble of Multilayer Perceptrons for Missing Electricity Consumption Data Imputation

Sensors ◽  
2020 ◽  
Vol 20 (6) ◽  
pp. 1772 ◽  
Author(s):  
Seungwon Jung ◽  
Jihoon Moon ◽  
Sungwoo Park ◽  
Seungmin Rho ◽  
Sung Wook Baik ◽  
...  
Keyword(s):  
Energy Consumption ◽  
Energy Management ◽  
Missing Values ◽  
Electric Energy ◽  
Electricity Consumption ◽  
Multilayer Perceptrons ◽  
Imputation Methods ◽  
Electric Energy Consumption ◽  
Consumption Data ◽  
Bagging Ensemble

For efficient and effective energy management, accurate energy consumption forecasting is required in energy management systems (EMSs). Recently, several artificial intelligence-based techniques have been proposed for accurate electric load forecasting; moreover, perfect energy consumption data are critical for the prediction. However, owing to diverse reasons, such as device malfunctions and signal transmission errors, missing data are frequently observed in the actual data. Previously, many imputation methods have been proposed to compensate for missing values; however, these methods have achieved limited success in imputing electric energy consumption data because the period of data missing is long and the dependency on historical data is high. In this study, we propose a novel missing-value imputation scheme for electricity consumption data. The proposed scheme uses a bagging ensemble of multilayer perceptrons (MLPs), called softmax ensemble network, wherein the ensemble weight of each MLP is determined by a softmax function. This ensemble network learns electric energy consumption data with explanatory variables and imputes missing values in this data. To evaluate the performance of our scheme, we performed diverse experiments on real electric energy consumption data and confirmed that the proposed scheme can deliver superior performance compared to other imputation methods.

scholarly journals Evaluation of Home Energy Efficiency Improvements in a Hot Desert Climate in Northwestern Mexico: The Energy Saving vs. Money Saving Conflict

Energies ◽  
2021 ◽  
Vol 14 (23) ◽  
pp. 7909
Author(s):  
Edgar Valenzuela ◽  
Hector Campbell ◽  
Gisela Montero ◽  
Marcos A. Coronado ◽  
Alejandro A. Lambert-Arista ◽  
...  
Keyword(s):  
Energy Consumption ◽  
Energy Conservation ◽  
Electronic Device ◽  
Electric Energy ◽  
Energy Utilization ◽  
Photovoltaic System ◽  
Conservation Strategies ◽  
Electrical Grid ◽  
Electric Energy Consumption ◽  
Northwestern Mexico

Reducing household energy consumption is one of the most important strategies used to decrease fossil fuel consumption and greenhouse gases emissions, and to encourage renewable energy utilization. Most energy conservation strategies in the domestic sector are aimed at preferential loans, i.e., purchasing renewable electricity or to improve the efficiency of home appliances, such as air conditioning and lighting. However, despite the relative economic successes of these technologies, they have not had expected impacts in regard to energy consumption. In this work, the authors analyzed the consumption patterns of two equivalent households—one was adapted with improved thermal insulation and a 1.2 kW photovoltaic system to reduce consumption from the electrical grid. The results show that dwellings where no improvements were made registered lower electric energy consumption, due the fact that users were aware that no strategy had been implemented, and its consumption; hence, electricity payments depended solely on one’s attention over the electronic device operations. On the other hand, energy conservation strategies in households promotes confident and relaxed attitudes toward the use of energy, leading to lower energy billings, but a higher gross energy consumption.

scholarly journals Statistical Approach in Analyzing of Advanced Metering Data in Power Distribution Grid

2019 ◽  
Vol 15 (2) ◽  
Author(s):  
Ivan Ramljak ◽  
Drago Bago
Keyword(s):  
Energy Consumption ◽  
Power Distribution ◽  
Distribution System ◽  
Statistical Approach ◽  
Electric Energy ◽  
Smart Metering ◽  
Distribution Grid ◽  
Smart Meters ◽  
Electric Energy Consumption ◽  
Advanced Metering

In last period many distribution system operators (DSO) invest significant amount of money in smart metering system. Those investments are in part due to regulatory obligations and in part due to needs of DSO (utilities) for knowledge about electric energy consumption. Term electric energy consumption refers not only on real consumption of electric energy but also on data about peak power, unbalance, voltage profiles, power losses etc. Data which DSO can have depends on type of smart metering system. Further, smart meters as source of data can be implemented in transformer stations (TS) MV/LV and in LV grid at consumer level. Generally, smart meters can be placed in any node of distribution grid. As amount of smart meters is greater, the possibility of data analysis is greater. In this paper a smart metering system of J.P Elektroprivreda HZ HB d.d, Mostar, Bosnia and Herzegovina will be presented. One statistical approach for analyzing of advanced metering data of TS MV/LV will be presented. Statistical approach presented here is powerful tool for analyzing great amount of data from distribution grid in simple way. Main contribution of this paper is in using results obtained from statistical analysis of smart meter data in distribution grid analyzing and in maintenance/investment planning.

scholarly journals MULTIPLE LINEAR REGRESSION APPROACH FOR SHORT-TERM FORECASTING OF ELECTRIC ENERGY CONSUMPTION IN TOGO

2020 ◽  
Vol 8 (10) ◽  
pp. 22-28
Author(s):  
Comlanvi Adjamagbo ◽  
◽  
Akim Adekunle Salami ◽  
Yao Bokovi ◽  
Djamil Gado ◽  
...  
Keyword(s):  
Energy Consumption ◽  
Electric Energy ◽  
Electrical Energy ◽  
Short Term ◽  
Electric Energy Consumption ◽  
Consumption Data ◽  
Regression Approach ◽  
Day Of The Week ◽  
Working Day ◽  
Short Term Forecasting

A Linear Multiple Regression approach is used to model the energy consumption of electricity in Togo. This model is developed from the load data recorded at the electric power source stations in Togo during the period from 2016 to 2017. This model predicts four input parameters (Day of the week, the type of day (working day). or not), Hours in the day and Load data of the same time of the previous day) is used to predict the electrical energy consumption data for the period of 2018 with a MAPE of 4.4964% and a correlation coefficient R2 equal to 95.5889%.

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