On hourly home peak load prediction

Singh, Rayman Preet; Lizotte, Daniel J.

doi:10.1109/smartgridcomm.2012.6485977

Cited by 45 publications

(28 citation statements)

References 17 publications

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“…At the individual home level, there are several studies on peak load prediction [33] and on energy disaggregation of individual appliances in households [23]. However, these datasets are much smaller (typically < 100 meters) than our current dataset.…”

Section: Introductionmentioning

confidence: 99%

Demand Forecasting in Smart Grids

Mirowski¹,

Sining²,

Ho³

et al. 2014

Bell Labs Tech. J.

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Data analytics in smart grids can be leveraged to channel the data downpour from individual meters into knowledge valuable to electric power utilities and end‐consumers. Short‐term load forecasting (STLF) can address issues vital to a utility but it has traditionally been done mostly at system (city or country) level. In this case study, we exploit rich, multi‐year, and high‐frequency annotated data collected via a metering infrastructure to perform STLF on aggregates of power meters in a mid‐sized city. For smart meter aggregates complemented with geo‐specific weather data, we benchmark several state‐of‐the‐art forecasting algorithms, including kernel methods for nonlinear regression, seasonal and temperature‐adjusted auto‐regressive models, exponential smoothing and state‐space models. We show how STLF accuracy improves at larger meter aggregation (at feeder, substation, and system‐wide level). We provide an overview of our algorithms for load prediction and discuss system performance issues that impact real time STLF. © 2014 Alcatel‐Lucent.

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Section: Introductionmentioning

confidence: 99%

Demand Forecasting in Smart Grids

Mirowski¹,

Sining²,

Ho³

et al. 2014

Bell Labs Tech. J.

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“…In [17], the peak load prediction over a small community of 24 homes is discussed, where the regression and the seasonal auto-regressive moving average models are applied. In [19], an adaptive regression splines method is used to predict the Hourly Ontario Energy Price (HOEP).…”

Section: Related Workmentioning

confidence: 99%

A statistical model for predicting power demand peaks in power systems

Cercone

2015

2015 12th International Conference on Fuzzy Systems and Knowledge Discovery (FSKD)

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The total commodity cost for electricity also includes the cost of building new electricity infrastructure and the expenses of providing conservation and demand response programs. This is called the Global Adjustment (GA) by the Independent Electricity System Operator (IESO), a corporate entity in Ontario, Canada working at the heart of Ontario's power system to ensure there is enough power to meet the province's energy needs in real-time and to plan and secure energy for the future. In Ontario, approximately 300 Class A customers representing Ontario's largest electricity consumers pay GA based on how much they contribute to the 5 highest peak hour demands in a fiscal year. Accurately predicting the top 5 peak hours in a fiscal year may help such a customer minimize its energy consumption in such periods and save it tens of millions of dollars in adjustment cost. In the meantime, this will reduce the size of demand peaks and the need of new electricity infrastructure for exceptionally high peak demands. This paper proposes to learn a statistical model for predicting in real-time the top 5 peak demand hours in a fiscal year, where feature selection is discussed. Preliminary experimental studies indicate the proposed model can effectively help locate the potential peak hours. This work is conducted for an application project, so we also discuss the implementation and deployment details of this model, where a client-server architecture with Ajax is adopted to ensure the updated peak hour information is delivered to all customers in real-time.

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“…• First, in a short time window (say 1 to 2 hours), the correlation between time and the load values may be hard to capture due to two reasons: (1) some random events may play a dominant role in electric load, and (2) [29].…”

Section: A Motivationmentioning

confidence: 99%

From Landscape to Portrait: A New Approach for Outlier Detection in Load Curve Data

Tang

Lei

et al. 2014

IEEE Trans. Smart Grid

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Abstract-In power systems, load curve data is one of the most important datasets that are collected and retained by utilities. The quality of load curve data, however, is hard to guarantee since the data is subject to communication losses, meter malfunctions, and many other impacts. In this paper, a new approach to analyzing load curve data is presented. The method adopts a new view, termed portrait, on the load curve data by analyzing the periodic patterns in the data and re-organizing the data for ease of analysis. Furthermore, we introduce algorithms to build the virtual portrait load curve data, and demonstrate its application on load curve data cleansing. Compared to existing regressionbased methods, our method is much faster and more accurate for both small-scale and large-scale real-world datasets.

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On hourly home peak load prediction

Cited by 45 publications

References 17 publications

Demand Forecasting in Smart Grids

Demand Forecasting in Smart Grids

A statistical model for predicting power demand peaks in power systems

From Landscape to Portrait: A New Approach for Outlier Detection in Load Curve Data

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