Adaptive neural networks for tariff forecasting and energy management

The paper looks at using a hybrid combination of recurrent neural networks trained with a temporal difference procedure for predicting local power tariff rates and energy use, with the intent of cost-effectively utilising electric power to heat the water in, for example, domestic hot water cylinder....

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Bibliographic Details
Main Authors: Wezenberg, H., Dewe, M.B.
Format: Conference Proceeding
Language:English
Subjects:
Adaptive systems
Energy consumption
Energy management
Engine cylinders
Load forecasting
Medical services
Neural networks
Resistance heating
Space heating
Water heating
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Summary:The paper looks at using a hybrid combination of recurrent neural networks trained with a temporal difference procedure for predicting local power tariff rates and energy use, with the intent of cost-effectively utilising electric power to heat the water in, for example, domestic hot water cylinder. The neural networks are adaptive and capable of both linear and non-linear time series forecasting with a minimum of training data.
DOI:10.1109/ICNN.1995.487534