Time series method for short-term load forecasting using smart metering in distribution systems

Ding, Ni; Bésanger, Yvon; Würtz, Fréderic; Gautier, Antoine

doi:10.1109/ptc.2011.6019331

Cited by 16 publications

(22 citation statements)

References 13 publications

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“…The similarity is usually based on day of the week, season of a year, and the weather patterns. While the similar day method is one of the earliest methods being applied to load forecasting, even today, many system operators are still having the load and temperature profiles of the representative days hanging on the wall of the operations room [7]. Modern similar day method is often implemented using some clustering techniques.…”

Section: Related Workmentioning

confidence: 99%

“…Modern similar day method is often implemented using some clustering techniques. Instead of one similar day, the algorithms may identify several similar days or similar segments of a day, and then combine them to obtain the forecasted load profile [7] [8].…”

Section: Related Workmentioning

confidence: 99%

“…Many research studies assume the electricity loads as the time series. Even the naïve, direct approaches, analyzing trends, cycles and errors of the time series, are considered as a part of a trending class [7]. More complex methods use the Artificial Neural Networks [3] [10] [11], and specifically developed statistical methods [12] [13].…”

Section: Related Workmentioning

confidence: 99%

See 2 more Smart Citations

Improved Short Term Energy Load Forecasting Using Web-Based Social Networks

Kantardzic¹,

Gavranovic²,

Gavranović³

et al. 2015

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In this article, we are initiating the hypothesis that improvements in short term energy load forecasting may rely on inclusion of data from new information sources generated outside the power grid and weather related systems. Other relevant domains of data include scheduled activities on a grid, large events and conventions in the area, equipment duty cycle schedule, data from call centers, real-time traffic, Facebook, Twitter, and other social networks feeds, and variety of city or region websites. All these distributed data sources pose information collection, integration and analysis challenges. Our approach is concentrated on complex non-cyclic events detection where detected events have a human crowd magnitude that is influencing power requirements. The proposed methodology deals with computation, transformation, modeling, and patterns detection over large volumes of partially ordered, internet based streaming multimedia signals or text messages. We are claiming that traditional approaches can be complemented and enhanced by new streaming data inclusion and analyses, where complex event detection combined with Webbased technologies improves short term load forecasting. Some preliminary experimental results, using Gowalla social network dataset, confirmed our hypothesis as a proof-of-concept, and they paved the way for further improvements by giving new dimensions of short term load forecasting process in a smart grid.

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Section: Related Workmentioning

confidence: 99%

Section: Related Workmentioning

confidence: 99%

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Improved Short Term Energy Load Forecasting Using Web-Based Social Networks

Kantardzic¹,

Gavranovic²,

Gavranović³

et al. 2015

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“…Such an algorithm is considerably more complex and for real time applications requires an accurate approximation of the loading and production in the next couple of hours. Several such algorithms have been developed [6,7].…”

Section: Figure 2 Optimal Charging and Discharging To Minimize Lossesmentioning

confidence: 99%

Technical dimensioning of an energy storage for a Swedish distribution company

Etherden

Ackeby

Bollen

et al. 2013

22nd International Conference and Exhibition on Electricity Distribution (CIRED 2013)

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“…Time series can be defined as "the evolution of a set of observations sampled at regular intervals along time. The specificity of time series models, compared to other statistic methods, is that it introduces 'time' as one of its explicative variables" [1]. Time series develop mathematical models that can forecast future observations on the basis of available data.…”

Section: Introductionmentioning

confidence: 99%

Time Series and Renewable Energy Forecasting

Ghofrani¹,

Alolayan²

2018

Time Series Analysis and Applications

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Renewable energy generation has been constantly increasing during recent years. Wind and solar have had the most significant growths among all renewable resources. Wind and solar resources are highly intermittent and dependent on meteorological parameters and climatic conditions. The power output of wind turbines is subject to various meteorological parameters, such as wind speed, wind direction, air temperature, relative humidity, etc., among which the wind speed is the most direct and influential factor in wind power generation. Solar photovoltaic (PV) power is a function of solar radiation. Wind speed and solar radiation time series data exhibit unique features which complicate their prediction. This makes wind and solar power forecasting challenging. Accurate wind and solar forecasting enhances the value of renewable energy by improving the reliability and economic feasibility of these resources. It also supports integrating solar and wind power into electric grids by reducing the integration and operation costs associated with these intermittent generation sources. This chapter provides an overview of the time series methods that can be used for more accurate wind and solar forecasting.

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Time series method for short-term load forecasting using smart metering in distribution systems

Cited by 16 publications

References 13 publications

Improved Short Term Energy Load Forecasting Using Web-Based Social Networks

Improved Short Term Energy Load Forecasting Using Web-Based Social Networks

Technical dimensioning of an energy storage for a Swedish distribution company

Time Series and Renewable Energy Forecasting

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