Modeling public holidays in load forecasting: a German case study

Ziel, Florian

doi:10.1007/s40565-018-0385-5

Cited by 49 publications

(24 citation statements)

References 48 publications

(63 reference statements)

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“…This improved the load forecasting accuracy. Reference [23] analyzed the difficulties in holiday load forecasting and proposed a series of approaches to deal with public holidays and weekends, and further drew a conclusion that the most promising approach is to add public holiday dummies to the model but set the weekday dummies at the holidays to zero. With this approach the load forecasting accuracy during holidays is greatly increased.…”

Section: Introductionmentioning

confidence: 99%

A learning framework based on weighted knowledge transfer for holiday load forecasting

Zeng

Sheng

Jin

2018

J. Mod. Power Syst. Clean Energy

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Since the variation pattern of load during holidays is different than that of non-holidays, forecasting holiday load is a challenging task. With a focus on this problem, we propose a learning framework based on weighted knowledge transfer for daily peak load forecasting during holidays. First, we select source cities which can provide extra hidden knowledge to improve the forecast accuracy of the load of the target city. Then, all the instances which are from source cities and the target city will be weighted and trained by the improved weighted transfer learning algorithm which is based on the TrA-daBoost algorithm and can decrease negative transfer. We evaluate our method with the classical support vector machine method and a method based on knowledge transfer on a real data set, which includes eleven cities from Guangdong province to illustrate the performance of the method. To solve the problem of limited historical holiday load data, we transfer the data from nearby cities based on the fact that nearby cities in Guangdong province have a similar economic development level and similar load variation pattern. The results of comparative experiments show that the forecasting framework proposed by this paper outperforms these methods in terms of mean absolute percent error and mean absolute scaled error.

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Section: Introductionmentioning

confidence: 99%

A learning framework based on weighted knowledge transfer for holiday load forecasting

Zeng

Sheng

Jin

2018

J. Mod. Power Syst. Clean Energy

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“…Below, we lay them out in order from what we believe to be the most obvious to the most speculative. Most directly, in [47], the authors showed that correctly accounting for holidays has a drastic positive impact both on holiday forecasts and on non-holiday periods. It would be straightforward to extend this model to include an indicator for holidays because Gaussian processes allow for indicators.…”

Section: Discussionmentioning

confidence: 99%

Intraday Load Forecasts with Uncertainty

2019

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We provide a comprehensive framework for forecasting five minute load using Gaussian processes with a positive definite kernel specifically designed for load forecasts. Gaussian processes are probabilistic, enabling us to draw samples from a posterior distribution and provide rigorous uncertainty estimates to complement the point forecast, an important benefit for forecast consumers. As part of the modeling process, we discuss various methods for dimension reduction and explore their use in effectively incorporating weather data to the load forecast. We provide guidance for every step of the modeling process, from model construction through optimization and model combination. We provide results on data from the largest deregulated wholesale U.S. electricity market for various periods in 2018. The process is transparent, mathematically motivated, and reproducible. The resulting model provides a probability density of five minute forecasts for 24 h.

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“…In [42], the authors show that correctly accounting for holidays has a drastic positive impact both on holiday forecasts and on non-holiday periods. While beyond the scope of this analysis, Gaussian processes do allow for indicators.…”

Section: Discussionmentioning

confidence: 99%

Intraday Load Forecasts with Uncertainty

Kozak¹,

Holladay²,

Fasshauer³

2019

Preprint

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We provide a comprehensive framework for forecasting five minute load using Gaussian processes with a positive definite kernel specifically designed for load forecasts. Gaussian processes are probabilistic, enabling us to draw samples from a posterior distribution and provide rigorous uncertainty estimates to complement the point forecast, an important benefit for forecast consumers. As part of the modeling process, we discuss various methods for dimension reduction and explore their use in effectively incorporating weather data to the load forecast. We provide guidance for every step of the modeling process, from model construction through optimization and model combination. We provide results on data from the PJMISO for various periods in 2018. The process is transparent, mathematically motivated, and reproducible. The resulting model provides a probability density of five-minute forecasts for 24 hours.

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Modeling public holidays in load forecasting: a German case study

Cited by 49 publications

References 48 publications

A learning framework based on weighted knowledge transfer for holiday load forecasting

A learning framework based on weighted knowledge transfer for holiday load forecasting

Intraday Load Forecasts with Uncertainty

Intraday Load Forecasts with Uncertainty

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