A novel model for home energy management system based on Internet of Things

Liu, Geng; Yi, Wei; Lu, Zhengding; Yang, Yu

doi:10.1109/icpre.2016.7871256

Cited by 10 publications

(5 citation statements)

References 14 publications

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“…This is accomplished through the use of a smart meter at the customer's location. The internet is modeled as a data exchange center for the SHEMS 12,13 . Mohammad et al 14 proposed a design for a grid‐connected smart home energy management system based on risk.…”

Section: Related Workmentioning

confidence: 99%

“…The internet is modeled as a data exchange center for the SHEMS. 12,13 Mohammad et al 14 proposed a design for a grid-connected smart home energy management system based on risk. The proposed method reduces consumer risk in the SHEMS problem by reducing market pricing uncertainty, using the genetic algorithm (GA) as an optimization method.…”

Section: Related Workmentioning

confidence: 99%

“…Calculate growth function g(t) using Equation (4). Calculate growth function g(t) using Equation (12). Calculate seasonality function s(t) using Equation ( 6).…”

Section: Dataset Processingmentioning

confidence: 99%

See 2 more Smart Citations

A novel Hypertuned Prophet based power saving approach for IoT enabled smart homes

Sharma

Jain

Dhingra

et al. 2022

Trans Emerging Tel Tech

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Recent advances in energy conversion, information technology, the internet, new types of web, and communication technologies have enabled the interconnection of all physical objects, including sensors and actuators. Web‐enabled smart objects have paved the way for smart homes by enabling innovative services. In this work, the idea of using time series analysis based on machine learning and forecasting considering the weather conditions is discussed, to enhance automation and improve intelligence. The proposed Prophet model is used to predict the future net energy consumption and generation for improving energy efficiency and enabling power backup. Energy efficiency is the need of the hour, wherein major utilization of energy is in the residential sector, it is gravely important to analyze current generation and consumption and act accordingly for the future predicted results. Moreover, smart homes are dependent on web technologies and telecommunication for the operation of every action, which makes it crucial to have necessary power backup. The Prophet forecasting model, after parameter tuning and logistic growth pattern with additional regressors, gives only 0.27 mean absolute error and 0.13 mean squared error for predicting future energy consumption as compared to other models.

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Section: Related Workmentioning

confidence: 99%

Section: Related Workmentioning

confidence: 99%

See 1 more Smart Citation

A novel Hypertuned Prophet based power saving approach for IoT enabled smart homes

Sharma

Jain

Dhingra

et al. 2022

Trans Emerging Tel Tech

View full text Add to dashboard Cite

show abstract

“…[26] presented an energy management design to optimize and control operations in a hybrid microgrid with real-time monitoring. The authors in [27] proposed the Internet of Things (IoT) model for home energy management systems. The data of the energy management system were provided by supervisory control, data acquisition and PMUs [28,29].…”

Section: Introductionmentioning

confidence: 99%

Optimizing Energy Management in Microgrids Based on Different Load Types in Smart Buildings

et al. 2022

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This paper presents an energy management strategy (EMS) based on the Stackelberg game theory for the microgrid community. Three agents or layers are considered in the proposed framework. The microgrid cluster (MGC) refers to the agent that coordinates the interactions between the microgrids and the utility grid. The microgrid agent manages the energy scheduling of its own consumers. The third agent represents the consumers inside the microgrids. The game equilibrium point is solved between different layers and each layer will benefit the most. First, an algorithm performs demand response in each microgrid according to load models in smart buildings and determines the load consumption for each consumer. Then, each microgrid determines its selling price to the consumers and the amount of energy required to purchase from the utility grid to achieve the maximum profit. Finally, the balance point will be obtained between microgrids by the microgrid cluster agent. Moreover, the proposed method uses various load types at different times based on real-life models. The result shows that considering these different load models with demand response increased the profit of the user agent by an average of 22%. The demand response is implemented by the time of use (TOU) model and real-time pricing (RTP) in the microgrid.

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“…A useful step in the reduction or the management of consumption as well as in the detection of events is the automatic generation of consumption maps, based on the identification of the consumers' characteristics, including the household consumers. The results can be used to generate consumption predictions with various applicability: energy saving [1], grid balancing by eliminating consumption peaks [2,3], local management of green energy sources [2,3], as well as detecting atypical behaviors that may be due to device failure [4], power theft [5], etc. In this context, the concept of signature was promoted.…”

Section: Introductionmentioning

confidence: 99%

A Novel Method for Obtaining the Signature of Household Consumer Pairs

Căiman

Dragomir

2020

Energies

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The management of electricity consumption by household consumers requires multiple ways of consumer monitoring. One of these is the signature i(v) determined by monitoring the consumer voltage-current trajectory. The paper proposes a novel method for obtaining signatures of 2-multiple consumers, i.e., a pair of consumers connected in parallel. Signatures are obtained from samples of the voltage at the consumers’ terminals and of the total current absorbed by the consumers, measured at a frequency of only 20 Hz. Within the method, signatures are calculated using genetic algorithms (GA) and nonlinear regression, according to a procedure developed by the authors in a previous paper. The management of the data selected for the signature assignment represents the novelty. The method proposed in this paper is applied in two case studies, one concerning household consumers within the same power level, the other for household consumers of different power levels. The results confirm the possibility of obtaining signatures of i(v) type.

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A novel model for home energy management system based on Internet of Things

Cited by 10 publications

References 14 publications

A novel Hypertuned Prophet based power saving approach for IoT enabled smart homes

A novel Hypertuned Prophet based power saving approach for IoT enabled smart homes

Optimizing Energy Management in Microgrids Based on Different Load Types in Smart Buildings

A Novel Method for Obtaining the Signature of Household Consumer Pairs

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