Analysis of the value for unit commitment of improved load forecasts

“…A study by Hobbs et al (1999) estimates cost savings in electricity generation from better forecasts of electricity loads. In the Hobbs et al study, actual loads and forecasted loads were obtained for two utility systems-one northeastern and one southern.…”

Section: Cost Savings From Improved Elec-tricity Demand (Or Load) Formentioning

confidence: 99%

“…Next, our estimates of the relationship between the quality of temperature forecasts and the quality of electricity demand forecasts is presented for four different temperature forecasts at six sites around the United States. Using these results in conjunction with the Hobbs et al (1999) work makes it possible to estimate the cost savings from different temperature forecasts at these sites. These site-specific cost savings are then extrapolated to estimate total benefits, and incremental benefits, for the United States as a whole.…”

mentioning

confidence: 99%

“…The paper begins with a discussion of relevant results from the earlier work by Hobbs et al (1999), which estimates the savings in electricity generation costs from improvements in electricity demand forecasts. Next, our estimates of the relationship between the quality of temperature forecasts and the quality of electricity demand forecasts is presented for four different temperature forecasts at six sites around the United States.…”

mentioning

confidence: 99%

See 1 more Smart Citation

The Economic Value of Temperature Forecasts in Electricity Generation

Teisberg¹,

Weiher²,

Khotanzad³

2005

Bull. Amer. Meteor. Soc.

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“…Keeping a low balancing cost is important due to low profit margins in the industry. A conservative estimate by (Hobbs et al, 1999) shows that a decrease of the load forecasting error in terms of mean absolute percentage error (MAPE) by 1 % lowers the variable production cost between 0.6 and 1.6 million USD annually for a 10,000 MW utility with MAPE around 4 %.…”

Section: Introductionmentioning

confidence: 99%

Load forecasting using a multivariate meta-learning system

Matijaš

Suykens

Krajcar

2013

Expert Systems with Applications

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Although over a thousand scientific papers address the topic of load forecasting every year, only a few are dedicated to finding a general framework for load forecasting that improves the performance, without depending on the unique characteristics of a certain task such as geographical location. Meta-learning, a powerful approach for algorithm selection has so far been demonstrated only on univariate time-series forecasting. Multivariate timeseries forecasting is known to have better performance in load forecasting. In this paper we propose a meta-learning system for multivariate time-series forecasting as a general framework for load forecasting model selection. We show that a meta-learning system built on 65 load forecasting tasks returns lower forecasting error than 10 well-known forecasting algorithms on 4 load forecasting tasks for a recurrent real-life simulation. We introduce new metafeatures of fickleness, traversity, granularity and highest ACF. The meta-learning framework is parallelized, component-based and easily extendable.

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Analysis of the value for unit commitment of improved load forecasts

Cited by 189 publications

References 11 publications

Short Term Load Forecasting Using a Neural Network Based Time Series Approach

Short Term Load Forecasting Using a Neural Network Based Time Series Approach

The Economic Value of Temperature Forecasts in Electricity Generation

Load forecasting using a multivariate meta-learning system

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