Time–Frequency Feature Combination Based Household Characteristic Identification Approach Using Smart Meter Data

Yan, Siqing; Li, Kangping; Wang, Fei; Ge, Xinxin; Lu, Xiaoxing; Mi, Zengqiang; Chen, Hongyu; Chang, Shengqiang

doi:10.1109/tia.2020.2981916

Cited by 73 publications

(8 citation statements)

References 31 publications

Supporting

Mentioning

Contrasting

Order By: Relevance

“…This information is useful especially for retailers to identify the customer who shares the same behavior. The profiling of customers can be used to provide them with better tariff rates [ 23 ].…”

Section: Proposed Methods and Materialsmentioning

confidence: 99%

Cluster Analysis and Model Comparison Using Smart Meter Data

Shaukat

Qadir

et al. 2021

Sensors

View full text Add to dashboard Cite

Load forecasting plays a crucial role in the world of smart grids. It governs many aspects of the smart grid and smart meter, such as demand response, asset management, investment, and future direction. This paper proposes time-series forecasting for short-term load prediction to unveil the load forecast benefits through different statistical and mathematical models, such as artificial neural networks, auto-regression, and ARIMA. It targets the problem of excessive computational load when dealing with time-series data. It also presents a business case that is used to analyze different clusters to find underlying factors of load consumption and predict the behavior of customers based on different parameters. On evaluating the accuracy of the prediction models, it is observed that ARIMA models with the (P, D, Q) values as (1, 1, 1) were most accurate compared to other values.

show abstract

Section: Proposed Methods and Materialsmentioning

confidence: 99%

Cluster Analysis and Model Comparison Using Smart Meter Data

Shaukat

Qadir

et al. 2021

Sensors

View full text Add to dashboard Cite

show abstract

“…Hypothetical examination demonstrates that the plan ensures clients' protection and has attributes of privacy and enforceability. The security of data downloaded by power organizations dependent on the consortium blockchain is a problem worth concentrating on later on [20,21]. Machine learning approaches like the Fuzzy system [22], Neural Network [23,24], DELM [25,26] and SVM [26] are robust candidate solutions in the field of smart health and smart city [24].…”

Section: Literature Reviewmentioning

confidence: 99%

Energy Demand Forecasting Using Fused Machine Learning Approaches

Ghazal¹,

Noreen²,

Said³

et al. 2022

Intelligent Automation &Amp; Soft Computing

View full text Add to dashboard Cite

The usage of IoT-based smart meter in electric power consumption shows a significant role in helping the users to manage and control their electric power consumption. It produces smooth communication to build equitable electric power distribution for users and improved management of the entire electric system for providers. Machine learning predicting algorithms have been worked to apply the electric efficiency and response of progressive energy creation, transmission, and consumption. In the proposed model, an IoT-based smart meter uses a support vector machine and deep extreme machine learning techniques for professional energy management. A deep extreme machine learning approach applied to feature-based data provided a better result. Lastly, decision-based fusion applied to both datasets to predict power consumption through smart meters and get better results than previous techniques. The established model smart meter with automatic load control increases the effectiveness of energy management. The proposed EDF-FMLA model achieved 90.70 accuracy for predicting energy consumption with a smart meter which is better than the existing approaches.

show abstract

“…In [49], the authors propose a meta-heuristic optimization-based two-stage residential load pattern clustering approach to handle unreasonable typical load pattern extraction. In [50], the authors propose a timefrequency feature combination based household characteristic identification approach using smart meter data. In [23], the authors quantify the full probability distribution function of flexibility in response to economic incentives considering the surrounding variables through the quantile regression method.…”

Section: ) Modeling Of Residential Customersmentioning

confidence: 99%

Novel Single Group-Based Indirect Customer Baseline Load Calculation Method for Residential Demand Response

Lee

Jang²,

et al. 2021

IEEE Access

View full text Add to dashboard Cite

Demand response (DR) is a voluntary program that encourages related stakeholders, in this case electricity consumers, to cut down on usage during periods of high electricity load. One key to fully exploiting DR is to encourage residential customers to join the DR program. Unlike in the DR programs for commercial and industrial customers, for the successful operation of the residential DR program, several issues have to be addressed, one of which is to provide a group-level incentive to participating customers. In particular, the issue comes up when the incentive calculated for a group is not equal to the aggregated incentives for each customer (i.e., non-equal incentive problem). The non-equal incentive problem deteriorates the successful operation of residential DR by decreasing the motivation of DR operators and customers. We first prove the non-equal incentive problem through mathematical and experimental methods. We then propose the novel single group-based indirect incentive calculation method. The basic idea of our approach is to indirectly calculate the incentive for each customer not using the customer's data but using the data of other customers of the same DR group. Through experiments involving the electricity usage data of 42,193 households and the real DR events in Korea, we show that our method solves the non-equal incentive problem in most cases. Furthermore, our method improves the accuracy of the baseline estimation (used for calculating the contribution).

show abstract

Time–Frequency Feature Combination Based Household Characteristic Identification Approach Using Smart Meter Data

Cited by 73 publications

References 31 publications

Cluster Analysis and Model Comparison Using Smart Meter Data

Cluster Analysis and Model Comparison Using Smart Meter Data

Energy Demand Forecasting Using Fused Machine Learning Approaches

Novel Single Group-Based Indirect Customer Baseline Load Calculation Method for Residential Demand Response

Contact Info

Product

Resources

About