Big data analytics for demand response: Clustering over space and time

Chelmis, Charalampos; Kolte, Jahanvi; Prasanna, Viktor K.

doi:10.1109/bigdata.2015.7364011

Cited by 50 publications

(24 citation statements)

References 17 publications

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“…Similarly, PCA was also used to find the temporal patterns of each consumer and spatial patterns of several consumers in [94]. Then, a modified K-medoids algorithm based on the Hausdorff distance and Voronoi decomposition method was proposed to obtain typical load profiles and detect outliers.…”

Section: Load Profilingmentioning

confidence: 99%

Review of Smart Meter Data Analytics: Applications, Methodologies, and Challenges

Wang

Chen

Hong

et al. 2019

IEEE Trans. Smart Grid

881

370

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The widespread popularity of smart meters enables an immense amount of fine-grained electricity consumption data to be collected. Meanwhile, the deregulation of the power industry, particularly on the delivery side, has continuously been moving forward worldwide. How to employ massive smart meter data to promote and enhance the efficiency and sustainability of the power grid is a pressing issue. To date, substantial works have been conducted on smart meter data analytics. To provide a comprehensive overview of the current research and to identify challenges for future research, this paper conducts an application-oriented review of smart meter data analytics. Following the three stages of analytics, namely, descriptive, predictive and prescriptive analytics, we identify the key application areas as load analysis, load forecasting, and load management. We also review the techniques and methodologies adopted or developed to address each application. In addition, we also discuss some research trends, such as big data issues, novel machine learning technologies, new business models, the transition of energy systems, and data privacy and security.

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Section: Load Profilingmentioning

confidence: 99%

Review of Smart Meter Data Analytics: Applications, Methodologies, and Challenges

Wang

Chen

Hong

et al. 2019

IEEE Trans. Smart Grid

881

370

View full text Add to dashboard Cite

show abstract

“…Spatial Big Data [46]) can be analyzed to find commonly frequented public areas [51], or can be made public to provide traffic information to users [26]. Power-consumption data from smart meters can be analyzed to extract typical daily consumption patterns in households [23], or to identify the right customers to target for demand response programs [8]. Personal data (e.g., age, gender, income, marital satisfaction) collected via survey sampling can be used to infer the statistics (e.g., histogram, heavy hitters) of a target population.…”

Section: Introductionmentioning

confidence: 99%

Toward Distribution Estimation under Local Differential Privacy with Small Samples

Murakami

Hino

Sakuma

2018

Proceedings on Privacy Enhancing Technologies

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A number of studies have recently been made on discrete distribution estimation in the local model, in which users obfuscate their personal data (e.g., location, response in a survey) by themselves and a data collector estimates a distribution of the original personal data from the obfuscated data. Unlike the centralized model, in which a trusted database administrator can access all users’ personal data, the local model does not suffer from the risk of data leakage. A representative privacy metric in this model is LDP (Local Differential Privacy), which controls the amount of information leakage by a parameter ∈ called privacy budget. When ∈ is small, a large amount of noise is added to the personal data, and therefore users’ privacy is strongly protected. However, when the number of users ℕ is small (e.g., a small-scale enterprise may not be able to collect large samples) or when most users adopt a small value of ∈, the estimation of the distribution becomes a very challenging task. The goal of this paper is to accurately estimate the distribution in the cases explained above. To achieve this goal, we focus on the EM (Expectation-Maximization) reconstruction method, which is a state-of-the-art statistical inference method, and propose a method to correct its estimation error (i.e., difference between the estimate and the true value) using the theory of Rilstone et al. We prove that the proposed method reduces the MSE (Mean Square Error) under some assumptions.We also evaluate the proposed method using three largescale datasets, two of which contain location data while the other contains census data. The results show that the proposed method significantly outperforms the EM reconstruction method in all of the datasets when ℕ or ∈ is small.

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“…Massive Online Analysis (MOA) is an open source framework for large data streams analysis, project that is the complement of WEKA for Big Data analysis. [3], [40], [44], [49], [64], [67], [84], [94], [129], [153], [160], [204], [212], [214], [245]), fuzzy c-means clustering (7) ( [49], [64], [173], [204], [245], [265], [266]), Hierarchical Clustering (HAC) (7) ( [44], [56], [64], [94], [204], [212], [232]), Support Vector Machine (SVM) (6) [3], [112], [150], [204], [239], [250], Self-Organising Map (SOM) (4) [2], [64], [167], [212], Multi Layer Perceptron (MLP) ANN (3) [40], [150], [232], t-means clustering [183], k-Nearest Neighbour (kNN) [112], [204], Random Forest…”

Section: Sms Resultsmentioning

confidence: 99%

Smart Grids Data Analysis: A Systematic Mapping Study

Rossi

Chren

2020

IEEE Trans. Ind. Inf.

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Article under review. This work has been submitted to the IEEE for possible publication. Copyright may be transferred without notice, after which this version may no longer be accessible.The article is structured as follows. In section II, we provide an overview of Smart Grids concepts: mainly architecture and components of the SG infrastructure. In section III, we define the goals, needs, process and research questions for the Systematic Mapping Study on data analysis in the SG context. In section IV, we present the results from the overall SMS process, divided into answers to the main research questions set. In Section V, we discuss the main threats to validity of the current study. Section VI provides the final conclusions.

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Big data analytics for demand response: Clustering over space and time

Cited by 50 publications

References 17 publications

Review of Smart Meter Data Analytics: Applications, Methodologies, and Challenges

Review of Smart Meter Data Analytics: Applications, Methodologies, and Challenges

Toward Distribution Estimation under Local Differential Privacy with Small Samples

Smart Grids Data Analysis: A Systematic Mapping Study

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