In this webinar, I'll present a white paper I wrote with my SAS colleagues. I will cover a few important ideas of utilizing the geographic hierarchy to reduce the load forecasting error. Please go to the webinar page to register if you are interested. The paper is downloadable with open access.
Citation
Sen-Hao Lai and Tao Hong, "When One Size No Longer Fits All: Electric Load Forecasting with a Geographic Hierarchy", SAS White Paper, 2013
Abstract
With the deployment of smart grid technologies, many utilities can now take advantage of hourly or sub-hourly data from millions of smart meters. There are many upsides to this, such as the fact that utilities can potentially charge customers different rates based on the time of day they use electricity. However, there are downsides as well: (1) many forecasting methodologies are outdated. (2) The days of one-size-fits-all models are gone for the utility forecaster. This talk tackles these issues through a case study with ISO New England data. In particular, we investigate how a number of approaches using geographic hierarchy and weather station data can improve the predictive analytics used to determine future electric usage. We also demonstrate why using geographic hierarchies is now imperative for utilities.
Citation
Sen-Hao Lai and Tao Hong, "When One Size No Longer Fits All: Electric Load Forecasting with a Geographic Hierarchy", SAS White Paper, 2013
When One Size No Longer Fits All: Electric Load Forecasting with a Geographic Hierarchy
Sen-Hao Lai and Tao Hong
Abstract
With the deployment of smart grid technologies, many utilities can now take advantage of hourly or sub-hourly data from millions of smart meters. There are many upsides to this, such as the fact that utilities can potentially charge customers different rates based on the time of day they use electricity. However, there are downsides as well: (1) many forecasting methodologies are outdated. (2) The days of one-size-fits-all models are gone for the utility forecaster. This talk tackles these issues through a case study with ISO New England data. In particular, we investigate how a number of approaches using geographic hierarchy and weather station data can improve the predictive analytics used to determine future electric usage. We also demonstrate why using geographic hierarchies is now imperative for utilities.
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