Using Smart Meter Data to Improve the Accuracy of Intraday Load Forecasting Considering Customer Behavior Similarities

Quilumba, Franklin L.; Lee, Wei-Jen; Huang, Heng; Wang, David Yanshi; Szabados, Robert Louis

doi:10.1109/tsg.2014.2364233

Cited by 432 publications

(206 citation statements)

References 23 publications

(31 reference statements)

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“…In [23], a k-means procedure is applied on features consisting in mean consumption for 5 well chosen periods of day, mean consumption per day of a week and peak position into the year. In each cluster a deep learning algorithm is used for forecasting and then the bottom up forecast is the simple sum of clusters forecasts.…”

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

“…This will largely affect the structure of the load and the load growth patterns around the country [4].…”

Section: Situations Of Market Environmentmentioning

confidence: 99%

Study of applicability of the methods of medium and long term load forecasting in new economy normal state

Zhao¹,

Zhou²,

Zhang³

et al. 2017

2nd Annual International Conference on Energy, Environmental &Amp; Sustainable Ecosystem Development (EESED 2016)

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Medium and long term load forecasting is important for power system planning and construction. Affected by the New Economy normal and other relevant factors, various regions of China have entered a new period of economic development mode and industrial structure changing. Load structure and load growth pattern, to a certain extent, has changed, which undoubtedly makes the long-term load forecasting more complex and uncertain. To study the models and methods of Medium and long term load forecasting in the market environment is more important. This paper deeply analyzes the new economy normal state's features, refines the factors which can properly characterize the new normal state's changing, and applies them to the medium and long term load forecasting's models; in accordance with the actual data of one province, uses a variety of methods and models to calculate and compare the predictions, and gets the applicability of long-term load forecasting model in new economy normal state.

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“…The loading profile of each distribution transformer is determined through aggregating energy consumption from customers downstream. The temporal relationship among different load patterns can be used for load forecasting [47].…”

Section: Smart Metering Technologymentioning

confidence: 99%

Electric Power Systems Research

2017

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Using Smart Meter Data to Improve the Accuracy of Intraday Load Forecasting Considering Customer Behavior Similarities

Cited by 432 publications

References 23 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

Study of applicability of the methods of medium and long term load forecasting in new economy normal state

Electric Power Systems Research

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