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.

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

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.

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 Internet of Things Based on Situation-Awareness for Energy Efficiency

2019 ◽  
Vol 12 (1) ◽  
pp. 28-53 ◽  
Author(s):  
Un Hee Schiefelbein ◽  
Diovane Soligo ◽  
Vinícius Maran ◽  
José Palazzo M. De Oliveira ◽  
João Carlos Damasceno Lima ◽  
...  
Keyword(s):  
Energy Efficiency ◽  
Energy Consumption ◽  
Internet Of Things ◽  
Environmental Impact ◽  
Real Time ◽  
Situation Awareness ◽  
Electric Energy ◽  
Energy Expenditures ◽  
Electric Energy Consumption

The reduction of electric energy consumption is considered as one of the main challenges in diverse sectors of the economy. To residential customers, the management of energy consumption can bring significant costs reduction and decreased environmental impact. This work presents a solution based on the use of situation-awareness applied in IOT that helps the users to reduce the consumption of electric energy through its own residence. The practical results obtained in the application of this proposal in a real-live scenario confirmed the option of collecting information directly of electrical appliances and inform the user of their energy expenditures in real-time, allowing the knowledge and the management of their expenses.

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%.

Interoperability Analysis and Expectation of Short Distance Micro-Power Wireless Communication Technology

2014 ◽  
Vol 530-531 ◽  
pp. 751-755
Author(s):  
Yun Feng Li ◽  
Yang Cao ◽  
Gang Dong
Keyword(s):  
Energy Consumption ◽  
Wireless Communications ◽  
Wireless Communication ◽  
Data Acquisition ◽  
Electric Energy ◽  
Future Research ◽  
Electric Energy Consumption ◽  
Consumption Data ◽  
The Status ◽  
Micro Power

Electric energy consumption data acquisition system is an important part of the smart grid, with the construction and development of the State Grid Corporation electric energy consumption data acquisition, micro-power wireless communication is becoming one of the important local communication technologies for electric energy consumption data acquisition. However, many micro-power wireless provider different standards also brought many problems. This article describes the status of the domestic energy consumption data acquisition, the characteristics of micro-power wireless communications Interconnection and Interworking as well as prospects for the future research directions in the field of micro-power wireless communications.

scholarly journals Artificial Intelligence, Accelerated in Parallel Computing and Applied to Nonintrusive Appliance Load Monitoring for Residential Demand-Side Management in a Smart Grid: A Comparative Study

2020 ◽  
Vol 10 (22) ◽  
pp. 8114
Author(s):  
Yu-Chen Hu ◽  
Yu-Hsiu Lin ◽  
Chi-Hung Lin
Keyword(s):  
Artificial Intelligence ◽  
Parallel Computing ◽  
Energy Consumption ◽  
Electric Energy ◽  
Evolutionary Computing ◽  
Demand Side ◽  
Electric Energy Consumption ◽  
Consumption Data ◽  
Load Monitoring ◽  
Load Disaggregation

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 consumers that manage their power demands according to received DR signals. Disaggregating composite electric energy consumption data from a single minimal set of plug-panel current and voltage sensors installed at the electric panel in a practical field of interest, nonintrusive appliance load monitoring (NIALM), a cost-effective load disaggregation approach for (residential) DSM, is able to discern individual electrical appliances concerned without accessing each of them by individual plug-load power meters (smart plugs) deployed intrusively. The most common load disaggregation approaches are based on machine learning algorithms such as artificial neural networks, while approaches based on evolutionary computing, metaheuristic algorithms considered as global optimization and search techniques, have recently caught the attention of researchers. This paper presents a genetic algorithm, developed in consideration of parallel evolutionary computing, and aims to address NIALM, whereby load disaggregation from composite electric energy consumption data is declared as a combinatorial optimization problem and is solved by the algorithm. The algorithm is accelerated in parallel, as it would involve large amounts of NIALM data disaggregated through evolutionary computing, chromosomes, and/or evolutionary cycles to dominate its performance in load disaggregation and excessively cost its execution time. Moreover, the evolutionary computing implementation based on parallel computing, a feed-forward, multilayer artificial neural network that can learn from training data across all available workers of a parallel pool on a machine (in parallel computing) addresses the same NIALM/load disaggregation. Where, a comparative study is made in this paper. The presented methodology is experimentally validated by and applied on a publicly available reference dataset.

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