Weathering power's demand

Using temperature forecasts to predict power demand has some major pitfalls. Here Martin Fischer and Michael Grossman suggest ways to glean more from forecasted temperature data

Temperature is the single most important predictor of electricity demand variability - and the variability of much else, for that matter. It is estimated that 25% of global GDP is directly influenced by weather and climate variability.1 In absolute terms, calendar factors, such as the day of the week or public holidays, may be responsible for a greater cyclicality in demand. However, these are easily incorporated in to existing models, and there is no forecasting science involved, as such. The

Only users who have a paid subscription or are part of a corporate subscription are able to print or copy content.

To access these options, along with all other subscription benefits, please contact info@risk.net or view our subscription options here: http://subscriptions.risk.net/subscribe

You are currently unable to copy this content. Please contact info@risk.net to find out more.

Sorry, our subscription options are not loading right now

Please try again later. Get in touch with our customer services team if this issue persists.

New to Risk.net? View our subscription options

Most read articles loading...

You need to sign in to use this feature. If you don’t have a Risk.net account, please register for a trial.

Sign in
You are currently on corporate access.

To use this feature you will need an individual account. If you have one already please sign in.

Sign in.

Alternatively you can request an individual account here