Load profiling and its application to demand response: A review

Wang, Yi; Chen, Qixin; Chen, Kang; Zhang, Mingming; Wang, Ke; Zhao, Yun

doi:10.1109/tst.2015.7085625

Cited by 240 publications

(114 citation statements)

References 47 publications

Supporting

Mentioning

Contrasting

Unclassified

Order By: Relevance

“…daily load curves) from a single individual or transversal when the goal is to build clusters of customers according to their load consumption profile and/or side information. The main application of clustering is load profiling which is essential for energy management, grid management and demand response (see [13]). For example, in [14] data mining techniques are applied to extract load profiles from individual load data of a set of low voltage Portuguese customers, and then supervised classification methods are used to allocate customers to the different classes.…”

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

View full text Add to dashboard Cite

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:

show abstract

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

View full text Add to dashboard Cite

show abstract

“…By taking into account all the days that belong to the same cluster with the selected day, for instance the 15 May 2009, the extraction of the price elasticity is held using similar days and hence, the seasonal variations and periodicities of the load are integrated. The hourly price elasticities using a linear, exponential and logarithmic model are given by the following expressions [46]:…”

Section: Price Elasticity Extractionmentioning

confidence: 99%

“…Load profiling is tested as an approach to derive representative or typical demand patterns [46,47]. The latter refers to the formulation of typical load curves for single consumers and groups of consumers.…”

mentioning

confidence: 99%

An Improved Fuzzy C-Means Algorithm for the Implementation of Demand Side Management Measures

et al. 2017

View full text Add to dashboard Cite

Abstract:Load profiling refers to a procedure that leads to the formulation of daily load curves and consumer classes regarding the similarity of the curve shapes. This procedure incorporates a set of unsupervised machine learning algorithms. While many crisp clustering algorithms have been proposed for grouping load curves into clusters, only one soft clustering algorithm is utilized for the aforementioned purpose, namely the Fuzzy C-Means (FCM) algorithm. Since the benefits of soft clustering are demonstrated in a variety of applications, the potential of introducing a novel modification of the FCM in the electricity consumer clustering process is examined. Additionally, this paper proposes a novel Demand Side Management (DSM) strategy for load management of consumers that are eligible for the implementation of Real-Time Pricing (RTP) schemes. The DSM strategy is formulated as a constrained optimization problem that can be easily solved and therefore, making it a useful tool for retailers' decision-making framework in competitive electricity markets.

show abstract

“…Issues associated with controlling the Demand Side Response of small end-users are widely described in the literature [14,15]. However, in most cases they relate the impact on the action, because of the considerable power dissipation intervention (a large number of end-user participating in the reduction program) must be stimulated, for example, through tariffs or technically aggregated, which may create legitimate concerns as to the effectiveness of the service.…”

Section: Introductionmentioning

confidence: 99%

“…Issues associated with controlling the Demand Side Response of small end-users are widely described in the literature [14,15]. However, in most cases they relate the impact on the demand side through tariff programs such as Time of Use (ToU), Real Time Pricing (RTP) or Critical Peak Pricing (CPP), where the price of electricity varies during the day and is associated with energy prices on the balancing market.…”

Section: Introductionmentioning

confidence: 99%

Distributed active demand response system for peak power reduction through load shifting

Benysek¹,

Jarnut²,

Werminski³

et al. 2016

Bulletin of the Polish Academy of Sciences Technical Sciences

View full text Add to dashboard Cite

Abstract. The large variability in power consumption in electrical power systems (EPS) influences not only growth balance losses and technical losses, but also in some cases reduces energy security. Delayed restoration of power generation, combined with unpredictable weather events leading to the loss of generating power can lead to a situation in which to save the stability of the power system there must be introduced in the system a load power limit or even disconnection of end-user in a given area, which will significantly reduce the comfort of use of energy. This situation can be prevented through either the building of new intervention power units or the aggregated use of new energy technologies, such as distributed network resources (DER), which are part of an intelligent Smart Grid network. Such resources bring together virtual power plants (VPP) and demand side management (DSM). The article presents an alternative decentralized active demand response (DADR) system, that by acting on selected groups of loads reduces peak loads with minimized loss of comfort of energy in use for the end-user. The system operates without any communication. The effectiveness of the proposed solution has been confirmed, outlined in test results obtained by the authors from a developed analytical model, which also contains stochastic algorithms to decrease the negative impact of such DSM systems on the power system (power overshoot and oscillation).

show abstract

Load profiling and its application to demand response: A review

Cited by 240 publications

References 47 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

An Improved Fuzzy C-Means Algorithm for the Implementation of Demand Side Management Measures

Distributed active demand response system for peak power reduction through load shifting

Contact Info

Product

Resources

About