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

Wang, Yi; Chen, Qixin; Hong, Tao; Chen, Kang

doi:10.1109/tsg.2018.2818167

“…Composing an exhaustive survey of recent realizations is then a difficult challenge not addressed here. As detailed in [3], individual consumption data analytics covers various fields of statistics and machine learning: time series, clustering, outlier detection, deep learning, matrix completion, online learning among others.…”

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

confidence: 99%

“…On the Irish data again, Ref. [3] propose to build an ensemble of forecasts from a hierarchical clustering on individual average weekly profiles, coupled with a deep learning model for forecasting in each cluster. Different forecasts corresponding to different sizes of the partition are at the end aggregated using linear regression.…”

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

confidence: 99%

See 1 more Smart Citation

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

Auder

¹

,

Cugliari

²

,

Goude

³

et al. 2018

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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 first section is dedicated to the industrial context and a review of individual electrical data analysis. Then, we focus on hierarchical time-series for bottom-up forecasting. The idea is to decompose the global signal and obtain disaggregated forecasts in such a way that their sum enhances the prediction. This is done in three steps: identify a rather large number of super-consumers by clustering their energy profiles, generate a hierarchy of nested partitions and choose the one that minimize a prediction criterion. Using a nonparametric model to handle forecasting, and wavelets to define various notions of similarity between load curves, this disaggregation strategy gives a 16% improvement in forecasting accuracy when applied to French individual consumers. Then, this strategy is implemented using R-the free software environment for statistical computing-so that it can scale when dealing with massive datasets. The proposed solution is to make the algorithm scalable combine data storage, parallel computing and double clustering step to define the super-consumers. The resulting software is openly available.

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“…Results showing a minimum gain of 11% in forecast accuracy are provided on the Irish data set and smart meter data from New-York. On the Irish data again, [3] propose to build an ensemble of forecasts from a hierarchical clustering on individual average weekly profiles, coupled with a deep learning model for forecasting in each cluster. Different forecasts corresponding to different sizes of the partition are at the end aggregated using linear regression.…”

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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“…However, these models only consider the user's response probability to the incentives, and the response time characteristics are not properly modeled. With the gradual popularization of smart meters and smart appliances, it has been possible to monitor and control electrical equipment remotely [25,26]. The smart power technology lays the foundation for small business users and even home users to participate in IL protocols.…”

Section: Introductionmentioning

confidence: 99%

Collaborative Optimal Pricing and Day-Ahead and Intra-Day Integrative Dispatch of the Active Distribution Network with Multi-Type Active Loads

Chen

¹

,

Xuan

²

,

Yang

³

et al. 2018

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Abstract:In order to better handle the new features that emerge at both ends of supply and demand, new measures are constantly being introduced, such as demand-side management (DSM) and prediction of uncertain output and load. However, the existing DSM strategies, like real-time price (RTP), and dispatch methods are optimized separately, and response models of active loads, such as the interruptible load (IL), are still imperfect, which make it difficult for the active distribution network (ADN) to achieve global optimal operation. Therefore, to better manage active loads, the response characteristics including both the response time and the responsibility and compensation model of IL for cluster users, and the real-time demand response model for price based load, were analyzed and established. Then, a collaborative optimization strategy of RTP and optimal dispatch of ADN was proposed, which can realize an economical operation based on mutual benefit and win-win mode of supply and demand sides. Finally, the day-ahead and intra-day integrative dispatch model using different time-scale prediction data was established, which can achieve longer-term optimization while reducing the impact of prediction errors on the dispatch results. With numerical simulations, the effectiveness and superiority of the proposed strategy were verified.

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

Cited by 918 publications

References 198 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

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

Collaborative Optimal Pricing and Day-Ahead and Intra-Day Integrative Dispatch of the Active Distribution Network with Multi-Type Active Loads

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