Identification of residential load profile in the Smart Grid context

Fernandes, Ricardo Augusto Souza; Silva, I. N.; Oleskovicz, Mário

doi:10.1109/pes.2010.5589653

Cited by 18 publications

(6 citation statements)

References 7 publications

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“…They are used for both prediction, and fault detection and diagnosis in HVAC. For example, data mining method is used to predict total building energy demand in [10] and to identify residential load in the smart grid context in [11]. A probabilistic approach combined with the free-running concept temperature are used to describe dynamic behavior by steady-state concepts and predict building energy use in [12].…”

Section: Introductionmentioning

confidence: 99%

Identifying important variables of energy use in low energy office building by using multivariate analysis

Djuric

Novaković

2012

Energy and Buildings

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The aim of the study was to indentify driving variables that contributed to energy use in a low energy office building by integrating building energy management system (BEMS) and energy use data. To take a further step towards zero emission buildings, it is necessary to identify what contributes the most to building energy use. Further, the idea was to encourage a smart use of BEMS data for energy use analysis. Principal component regression and partial least squares regression were used for the data analysis. Databases of 76 and 41 variables respectively, which included occupancy level, control signals, and water and air temperatures, were used to explain heating, electricity, and fan energy use. Variable contributions to the principal components were used to simplify the model and to find the most important variables. In this way, energy use was defined indirectly by using available variables in BEMS. The approach was tested on a low energy office building. The results showed that important variables were different for different months in the case of heating energy use. The total electricity and fan electricity use could be defined with the same variables in different months. The total electricity use could be defined by using occupancy level and input fan signals. The suggested approach could be used by building operators to identify opportunities for decreasing energy use and for energy use estimation when data are lost due to data transmission issues or other problems. A relationship between building information and energy use was established.

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Section: Introductionmentioning

confidence: 99%

Identifying important variables of energy use in low energy office building by using multivariate analysis

Djuric

Novaković

2012

Energy and Buildings

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“…The same simulator with enhanced capabilities is presented in [57], with improved communication infrastructure/protocol and simulation speed. A hybrid approach of software and laboratory based test-bed which uses Internet as underlying communication infrastructure between utility and residential consumers in presented in [69]. The objective is to identify residential load profile whereas, residential consumers and utility are represented by client and server software.…”

Section: Mas and Non Mas-based Approachesmentioning

confidence: 99%

Demand Side Simulation: Architecture and Performance

Yousaf¹

2023

Preprint

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In this thesis, we describe a simulator of residential power consumption, as the first step towards a comprehensive demand-side simulator in the context of smart grid. The si mulator uses a commercialRelational Database Management System (RDBMS) as its simulation engine, and is thus capable of supporting much larger simulated systems than other existing simulators which are mostly based on a multi-agent simulation paradigm. The RDBMS-based design also leads to much improved performance while requiring less resources than comparable MAS-based system. Moreover, simulator records all the events which exceed a certain threshold and in response controls demand and assures the stability of the system, based on the future and past events. furthermore, simulator can help utility companies to obtain initial data that can lead to the development of more complex solutions to monitor and control energy consumption, and thus identify target operating points for the generation and distribution systems, with the ultimate goal of balancing the demand and supply, and of improving energy efficiency at the utility level.

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“…The benefit of the proposed methodology relies on the exhaustive utilization of the harmonic content of each load under a simple and robust formation that could provide higher efficiency when referring to more loads with similar nominal values but different electrical characteristics. Moreover, in [23] an approach using current harmonic signatures is presented. The disadvantage of this method relies on utilization of one Artificial Neural Network ANN for each appliance, which presumes training and appropriate preprocess of the initial data (not all harmonic signatures utilized because in such case the solution would not be generalized).…”

Section: Algorithm Implementationmentioning

confidence: 99%

“…In [19], only three loads are examined and despite the utilization of a GA, the identification rates for the energizing case study of the loads is lower than 95%. Furthermore in [26], an evolution of [23] is presented. The authors have increased the sampling from 1 kHz to 20 kHz for a disaggregation case study.…”

Section: Algorithm Implementationmentioning

confidence: 99%