An artificial neutral network hourly temperature forecaster with applications in load forecasting
- Southern Methodist Univ., Dallas, TX (United States). Electrical Engineering Dept.
- Electric Power Research Inst., Palo Alto, CA (United States). Power Delivery Group
Many short term load forecasting techniques use forecast hourly temperatures in generating a load forecast. Some utility companies, however, do not have access to a weather service that provides these forecasts. To fill this need, a temperature forecaster, based on artificial neural networks, has been developed that predicts hourly temperatures up to seven days in the future. The prediction is based on forecast daily high and low temperatures and other information that would be readily available to any utility. The forecaster has been evaluated using data from eight utilities in the US. The mean absolute error of one day ahead forecasts for these utilities is 1.48{degree}F. The forecaster is implemented at several electric utilities and is being used in production environments.
- OSTI ID:
- 264259
- Report Number(s):
- CONF-950727--
- Journal Information:
- IEEE Transactions on Power Systems, Journal Name: IEEE Transactions on Power Systems Journal Issue: 2 Vol. 11; ISSN ITPSEG; ISSN 0885-8950
- Country of Publication:
- United States
- Language:
- English
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