Local Short and Middle Term Electricity Load Forecasting With Semi-Parametric Additive Models

Goude, Yannig; Nedellec, Raphaël; Kong, Nicolas

doi:10.1109/tsg.2013.2278425

Cited by 189 publications

(108 citation statements)

References 17 publications

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“…Reference [45] use such a way of modeling with two groups of public holidays, but the grouping itself is not reported. Also [46] …”

Section: Replacing Public Holiday Dummiesmentioning

confidence: 99%

Modeling public holidays in load forecasting: a German case study

Ziel

2018

J. Mod. Power Syst. Clean Energy

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We address the issue of public or bank holidays in electricity load modeling and forecasting. Special characteristics of public holidays such as their classification into fixed-date and weekday holidays are discussed in detail. We present state-of-the-art techniques to deal with public holidays such as removing them from the data set, treating them as Sunday dummy or introducing separate holiday dummies. We analyze pros and cons of these approaches and provide a large load forecasting study for Germany that compares the techniques using standard performance and significance measures. Finally, we give general recommendations for the treatment of public holidays in energy forecasting to suggest how the drawbacks particular to most of the state-of-the-art methods can be mitigated. This is especially useful, as the incorporation of holiday effects can improve the forecasting accuracy during public holidays periods by more than 80%, but even for non-holidays periods, the forecasting error can be reduced by approximately 10%.

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“…Reference [45] use such a way of modeling with two groups of public holidays, but the grouping itself is not reported. Also [46] …”

Section: Replacing Public Holiday Dummiesmentioning

confidence: 99%

Modeling public holidays in load forecasting: a German case study

Ziel

2018

J. Mod. Power Syst. Clean Energy

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“…Equation 1 is estimated using penalised cubic splines (Wood, 2006;Goude et al, 2014), which is expressed in terms of Equation 3.…”

Section: Generalised Additive Modelsmentioning

confidence: 99%

“…Results from this study showed that the proposed model performs better than the operational one. A generalised additive modelling framework was proposed by Goude et al (2014) to model electricity demand in France on 2260 sub-stations across the country, on both short-and middle-term horizons. Empirical results from this study showed good performance in both cases, i.e.…”

Section: Introductionmentioning

confidence: 99%

Forecasting medium-term electricity demand in a South African electric power supply system

Sigauke

2017

J. energy South. Afr.

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“…Electricity production and consumption were investigated to forecast electricity load consumption in [5] using a genetic algorithm, in [6] using a data-based methodology, and in [7] using support vector machine for regression (SVR) and multilayer perceptron (MLP) for district or single household level. Whereas in [8], a semi-parametric approach based on generalized additive models theory was suggested. In [9], an incremental time series clustering technique using ARIMA time series forecasting model, along with a novel affinity score for determining cluster similarity of time series datasets was proposed.…”

Section: Related Workmentioning

confidence: 99%

A Mobile-based Platform for Big Load Profiles Data Analytics in Non-Advanced Metering Infrastructures

Moussa

2016

MATEC Web Conf.

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Abstract. With the rapidly increase of electricity demand around the world due to industrialization and urbanization, this turns the availability of precise knowledge about the consumption patterns of consumers to a valuable asset for electricity providers, given the current competitive electricity market. This would allow them to provide satisfactory services in time of load peaks and to control fraud and abuse cases. Despite of this crucial necessity, this is currently very hard to achieve in many developing countries since smart meters or advanced metering infrastructures (AMIs) are not yet settled there to monitor and report energy usages. Whereas the communication and information technologies have widely emerged in such nations, allowing the enormous spread of smart devices among population. In this paper, we present mobile-based BLPDA, a novel platform for big data analytics of consumers' load profiles (LPs) in the absence of AMIs' establishment. The proposed platform utilizes mobile computing in order to collect the consumptions of consumers, build their LPs, and analyze the aggregated usages data. Thus, allowing electricity providers to have better vision for an enhanced decision making process. The experimental results emphasize the effectiveness of our platform as an adequate alternative for AMIs in developing countries with minimal cost.

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Local Short and Middle Term Electricity Load Forecasting With Semi-Parametric Additive Models

Cited by 189 publications

References 17 publications

Modeling public holidays in load forecasting: a German case study

Modeling public holidays in load forecasting: a German case study

Forecasting medium-term electricity demand in a South African electric power supply system

A Mobile-based Platform for Big Load Profiles Data Analytics in Non-Advanced Metering Infrastructures

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