A review of electric load classification in smart grid environment

Zhou, Kaile; Yang, Shanlin; Shen, Chao

doi:10.1016/j.rser.2013.03.023

Cited by 229 publications

(97 citation statements)

References 45 publications

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“…[11] and [12] give an overview of the clustering techniques for customer grouping, finding patterns into electricity load data or detecting outliers and apply it to 400 non-residential medium voltage customers. Clustering can be seen as longitudinal when the objective is to cluster temporal patterns (e.g.…”

Section: Individual Electrical Consumption Data: a State-of-the-artmentioning

confidence: 99%

Scalable Clustering of Individual Electrical Curves for Profiling and Bottom-up Forecasting

Auder¹,

Cugliari²,

Goude³

et al. 2018

Preprint

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Smart grids require flexible data driven forecasting methods. We propose clustering tools for bottom-up short-term load forecasting. We focus on individual consumption data analysis which plays a major role for energy management and electricity load forecasting. The two first sections are dedicated to the industrial context and a review of individual electrical data analysis. We are interested in hierarchical time-series for bottom-up forecasting. The idea is to disaggregate the signal in such a way that the sum of disaggregated forecasts improves the direct prediction. The 3-steps strategy defines numerous super-consumers by curve clustering, builds a hierarchy of partitions and selects the best one minimizing a forecast criterion. Using a nonparametric model to handle forecasting, and wavelets to define various notions of similarity between load curves, this disaggregation strategy applied to French individual consumers leads to a gain of 16% in forecast accuracy. We then explore the upscaling capacity of this strategy facing massive data and implement proposals using R, the free software environment for statistical computing. The proposed solutions to make the algorithm scalable combines data storage, parallel computing and double clustering step to define the super-consumers.Keywords: Clustering; Forecasting; Hierarchical Time-Series; Individual Electrical Consumers; Scalable; Short Term; Smart Meters; Wavelets Industrial contextEnergy systems are facing a revolution and many challenges. On the one hand, electricity production is moving to more intermittency and complexity with the increase of renewable energy and the development of small distributed production units such as photovoltaic panels or wind farms. On the other hand, consumption is also changing with plug-in (hybrid) electric vehicles, heat pumps, the development of new technologies such as smart phones, computers, robots that often come with batteries. To maintain the electricity quality, energy stakeholders are developing smart grids (see [1,2] This results into new opportunities such as local optimisation of the grid, demand side management and smart control of storage devices. Exploiting the smart grid efficiently requires advanced data analytics and optimisation techniques to improve forecasting, unit commitment, and load planning at different geographical scales. Massive data sets are and will be produced as Preprints (www.preprints.org) | NOT PEER-REVIEWED | Posted:

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Section: Individual Electrical Consumption Data: a State-of-the-artmentioning

confidence: 99%

Scalable Clustering of Individual Electrical Curves for Profiling and Bottom-up Forecasting

Auder¹,

Cugliari²,

Goude³

et al. 2018

Preprint

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show abstract

“…Maximum investigation relate to relevant data mining correlating with Load Classification (LC), Predictive analytics. Also correction of Bad data, scheduling of optimal energy resources and locate for Power prices [9], [10].The productive processing of the enormous data requires expanded storage of data and evaluating of resources, which involve the demand of high performance computing (HPC) approaches. DEM estimates the electricity cost and sets corrects the prices of electricity by enabling and interrelating to the energy demands and the prices of the electricity [28].…”

Section: Literature Surveymentioning

confidence: 99%

Generation of dynamic energy management using data mining techniques basing on big data analytics isssues in smart grids

Lydia¹,

Kumar²,

Ramya³

2018

IJET

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The Optimal bidirectional flow of the electric power and the communicational data between suppliers and consumers are greatly enabled by the Smart Electricity in Grid. Reliable and Feasible micro energy generated due to Dynamic Energy Management (DEM) and the electricity market by consumers and suppliers. The smart grid features ICCM, aims to bring out the power at reduced cost. Powerful and practical DEM relies on load and sustainable production. Smart meters attain the huge data quantity through practical methods and solutions in this real world working. Smart Grids are enhanced by the operations such as data analytics, giving out high performance estimation, Adequate data network management and cloud computing. This paper aims focusthe issuesin big data and challenges experienced by the Dynamic Energy Management signed in Smart Grid. A detail explanation of data processing techniques that are mostly implemented and It also provides a brief description of the most commonly used data processing methods and recommended proposes a upcoming future directional research in thefield.

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“…Also discussed are future trends in Data Science, which will lead to new methods and tools capable of the more intelligent processing of large amounts of data collected from multiple distributed devices. Although there are other reviews on automatic techniques for building efficiency assessment [4,5], and on classification methods for load and energy consumption prediction [6], this work examines and discusses a broader set of data science techniques, and their applications to the different aspects of building energy management.…”

Section: Introductionmentioning

confidence: 99%

Data science for building energy management: A review

Molina-Solana

Ros

Ruiz

et al. 2017

Renewable and Sustainable Energy Reviews

227

108

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The energy consumption of residential and commercial buildings has risen steadily in recent years, an increase largely due to their HVAC systems. Expected energy loads, transportation, and storage as well as user behavior influence the quantity and quality of the energy consumed daily in buildings. However, technology is now available that can accurately monitor, collect, and store the huge amount of data involved in this process. Furthermore, this technology is capable of analyzing and exploiting such data in meaningful ways. Not surprisingly, the use of data science techniques to increase energy efficiency is currently attracting a great deal of attention and interest. This paper reviews how Data Science has been applied to address the most difficult problems faced by practitioners in the field of Energy Management, especially in the building sector. The work also discusses the challenges and opportunities that will arise with the advent of fully connected devices and new computational technologies.

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A review of electric load classification in smart grid environment

Cited by 229 publications

References 45 publications

Scalable Clustering of Individual Electrical Curves for Profiling and Bottom-up Forecasting

Scalable Clustering of Individual Electrical Curves for Profiling and Bottom-up Forecasting

Generation of dynamic energy management using data mining techniques basing on big data analytics isssues in smart grids

Data science for building energy management: A review

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