The Power of Big Data and Data Analytics for AMI Data: A Case Study

Guerrero-Prado, Jenniffer S.; Alfonso-Morales, Wilfredo; Caicedo-Bravo, Eduardo F.; Zayas-Pérez, Benjamín; Espinosa-Reza, Alfredo

doi:10.3390/s20113289

Cited by 26 publications

(9 citation statements)

References 53 publications

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“…The Big Data Analytics developments implemented in that previous study case are load forecasting, customer clustering, and modeling and identifying atypical consumption. We already detailed the implementation and results of the case study in a previous number of this Sensors Journal [ 41 ].…”

Section: Framework Validation Results: a Case Studymentioning

confidence: 99%

“…We validated this framework through two case studies. The first case about the analysis of electricity consumption data from smart meters in London from 2011 to 2014 was already reported in [ 41 ]. Section 3 presents the second one, which implements each component of the framework for a load forecasting application in a Colombian Retail Electricity Provider (REP).…”

Section: Introductionmentioning

confidence: 99%

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A Data Analytics/Big Data Framework for Advanced Metering Infrastructure Data

Guerrero-Prado,

Alfonso-Morales,

Caicedo-Bravo

2021

Sensors

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The Advanced Metering Infrastructure (AMI) data represent a source of information in real time not only about electricity consumption but also as an indicator of other social, demographic, and economic dynamics within a city. This paper presents a Data Analytics/Big Data framework applied to AMI data as a tool to leverage the potential of this data within the applications in a Smart City. The framework includes three fundamental aspects. First, the architectural view places AMI within the Smart Grids Architecture Model-SGAM. Second, the methodological view describes the transformation of raw data into knowledge represented by the DIKW hierarchy and the NIST Big Data interoperability model. Finally, a binding element between the two views is represented by human expertise and skills to obtain a deeper understanding of the results and transform knowledge into wisdom. Our new view faces the challenges arriving in energy markets by adding a binding element that gives support for optimal and efficient decision-making. To show how our framework works, we developed a case study. The case implements each component of the framework for a load forecasting application in a Colombian Retail Electricity Provider (REP). The MAPE for some of the REP’s markets was less than 5%. In addition, the case shows the effect of the binding element as it raises new development alternatives and becomes a feedback mechanism for more assertive decision making.

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Section: Framework Validation Results: a Case Studymentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

A Data Analytics/Big Data Framework for Advanced Metering Infrastructure Data

Guerrero-Prado,

Alfonso-Morales,

Caicedo-Bravo

2021

Sensors

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“…The DR classification as given by the US Department of Energy [22] is shown in Figure 4, and its functional strategy is shown in Figure 5. Utility sends the DR request to the consumer via Advanced Metering Infrastructure (AMI), which is an integration of smart meters, communication networks, measurement terminals, data concentrators, and data management systems [23]. AMI replaces the conventional meters with smart meters to promote two-way communication for remote monitoring and control applications.…”

Section: Demand Responsementioning

confidence: 99%

A Comprehensive Review on Residential Demand Side Management Strategies in Smart Grid Environment

et al. 2021

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The ever increasing demand for electricity and the rapid increase in the number of automatic electrical appliances have posed a critical energy management challenge for both utilities and consumers. Substantial work has been reported on the Home Energy Management System (HEMS) but to the best of our knowledge, there is no single review highlighting all recent and past developments on Demand Side Management (DSM) and HEMS altogether. The purpose of each study is to raise user comfort, load scheduling, energy minimization, or economic dispatch problem. Researchers have proposed different soft computing and optimization techniques to address the challenge, but still it seems to be a pressing issue. This paper presents a comprehensive review of research on DSM strategies to identify the challenging perspectives for future study. We have described DSM strategies, their deployment and communication technologies. The application of soft computing techniques such as Fuzzy Logic (FL), Artificial Neural Network (ANN), and Evolutionary Computation (EC) is discussed to deal with energy consumption minimization and scheduling problems. Different optimization-based DSM approaches are also reviewed. We have also reviewed the practical aspects of DSM implementation for smart energy management.

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“…Initially, power grids only included operations of production, transmission and distribution of energy, the capacities of which were not sufficient to ideally meet energy requirements. In recent years, these classical grid infrastructures have gradually undergone a transformation by a combination of digital systems referring to the notion of smart grid (Guerrero-Prado et al, 2020;Wang et al, 2018). The advent of the smart grid, considered to be the electricity grid of the future, comprising several new technologies, makes it possible to ensure better management of energy from generation sources to consumers, thus ensuring supply of energy demand.…”

Section: Introductionmentioning

confidence: 99%

Forecasting of Electrical Energy Consumption of Households in a Smart Grid

Souhe

Mbey²,

Boum³

et al. 2021

IJEEP

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This paper aims to develop a hybrid model for forecasting electrical energy consumption of households based on a Particle Swarm Optimization (PSO) algorithm associated with the Grey and Adaptive Neuro-Fuzzy Inference System (ANFIS). This paper proposes a new Grey-ANFIS-PSO model that is based on historical data from smart meters in order to estimate and improve the accuracy of forecasting electrical energy consumption. This accuracy will be characterized by coefficients such as Root Mean Square Error (RMSE), Mean Absolute Error (MAE) and Mean Absolute Percentage Error (MAPE). The PSO will allow to optimally design the Neuro-fuzzy forecasting. This method is implemented on Cameroon consumption data over the 24-years period in order to forecast energy consumption for the next years. Using this model, we were able to estimate that electricity consumption will be 1867 GWH in 2028 with 0.20158 RMSE and 0.62917% MAPE. The simulation results obtained show that implementation of this new optimized Neuro-fuzzy model on consumption data for a long period presents better results on prediction of electrical energy consumption compared to single artificial intelligence models of literature such as Support Vector Machine (SVM) and Artificial Neural Network (ANN).

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The Power of Big Data and Data Analytics for AMI Data: A Case Study

Cited by 26 publications

References 53 publications

A Data Analytics/Big Data Framework for Advanced Metering Infrastructure Data

A Data Analytics/Big Data Framework for Advanced Metering Infrastructure Data

A Comprehensive Review on Residential Demand Side Management Strategies in Smart Grid Environment

Forecasting of Electrical Energy Consumption of Households in a Smart Grid

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