Research Advancements Towards in Existing Smart Metering over Smart Grid

A, Abdul Khadar; Khan, Javed Ahamed; Nagaraj, M. S.

doi:10.14569/ijacsa.2017.080511

Cited by 9 publications

(3 citation statements)

References 32 publications

(33 reference statements)

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“…Both the inputs and the interactions are specified for each phase of the framework. In the Planning phase, users state their objectives, potentially on the HEMS' (graphical) user interface (e.g., (Han et al 2014;Khadar et al 2017;Yener et al 2017)), as this interface is a suitable way to enable the user to transport his or her objectives into the framework. This phase occurs either on the initial use of the DR method or when an "Objective Event" occurs.…”

Section: Embed the Frameworkmentioning

confidence: 99%

“…DR methods are here understood as algorithms that are developed to solve optimization problems in the DR context in light of constraints and desired goals. However, (Khadar et al 2017) denotes, that no sophisticated comparison of DR method performance is done yet. Moreover, to the best of our knowledge, no approaches have been developed so far that support this comparison and a selection of best performing DR methods for a user-specific scenario.…”

mentioning

confidence: 99%

Empowering the selection of demand response methods in smart homes: development of a decision support framework

et al. 2018

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Demand Response (DR) facilitates the monitoring and management of appliances in energy grids by employing methods that, for example, increase the reliability of energy grids and reduce users' cost. Within energy grids, Smart Home scenarios can be characterized by a unique combination of appliances and user preferences. To increase their impact, a scenario-specific selection of the best performing DR methods is necessary. As the user faces a multitude of heterogeneous DR methods to choose from, a complex decision problem is present. The primary goal of this study is to develop a decision support framework that can determine the bestperforming DR methods. Building on literature analyses, expert workshops and expert interviews, we identify seven requirements, derive solution concepts addressing these requirements, and develop the framework by combining the concepts using a benchmarking process as a template. To demonstrate the framework's applicability, we conduct a simulation study that uses artificial (simulated) data for seven types of households. Within this study, we employ four DR methods, assume changing appliances over time and cost minimization as primary objective. The study indicates, that by using the framework and thus by identifying and using the best DR method for each scenario, the users can achieve further cost benefits. The application of the framework allows practitioners to increase the efficiency of the DR method selection process and to further enhance DR-related benefits, such as cost minimization, load profile flattening, and peak load reduction. Researchers benefit from guidance for benchmarking and evaluating DR methods.

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“…The use of smart meters is not significant in some countries in developing state due their installation cost [1,2]. That is why, this research paper has treated the consumed energy forecasting goal to enhance the reliability of forecasted energy and the dynamic forecasting algorithms robustness, by the implementation of the necessary algorithms responsible for the energy forecasting [3][4] [5][6] [7].…”

Section: Introductionmentioning

confidence: 99%

Novel Training Methods Based ANN for the Consumed Energy Forecasting

Farhat¹,

Cherif²

2022

Advances in Intelligent Systems Research

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Artificial Neural Networks have demonstrated best effectiveness and excellent scheduling capabilities in realizing many purposes like recognition, clustering, classification, management and even prediction. For this reason, we have used RBF based Artificial NN for the dynamic forecasting of load and Photovoltaic production using many operations like forecasting, training and validation of the data accuracy. For the validation, the Mean Absolute Percent Error is calculated in function of the most three relevant input parameters, which are previous load and Photovoltaic production measurements, seasonability and temperature or solar radiation data. This work has used real-time measurements of load and Photovoltaic production for their comparison with the predicted load data using RBFNN algorithms for the calculation of MAE and MAPE, to deduce the performance of forecasting algorithms including the accuracy of the forecasted data. This research paper has treated 2 goals. The first is the short-term energy and Photovoltaic production forecasting including training operations. The 2nd goal is the calculation of Mean Absolute Error and Mean Absolute Percent Error via the comparison between the forecasted data and real-time measurements to evaluate the reliability of forecasted data and the performance of the forecasting algorithms. By this way, the dynamic prediction algorithms were implemented, the predicted data were compared to the same time-series measurements and forecasted energy MAPE was calculated.

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Research Advancements Towards in Existing Smart Metering over Smart Grid

Cited by 9 publications

References 32 publications

Empowering the selection of demand response methods in smart homes: development of a decision support framework

Novel Training Methods Based ANN for the Consumed Energy Forecasting

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