Title :
Adaptive neural networks for tariff forecasting and energy management
Author :
Wezenberg, H. ; Dewe, M.B.
Author_Institution :
Dept. of Electr. & Electron. Eng., Canterbury Univ., Christchurch, New Zealand
Abstract :
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
Keywords :
forecasting theory; power consumption; power utilisation; recurrent neural nets; tariffs; time series; adaptive neural networks; energy management; energy use; recurrent neural networks; tariff forecasting; temporal difference procedure; time series forecasting; Adaptive systems; Energy consumption; Energy management; Engine cylinders; Load forecasting; Medical services; Neural networks; Resistance heating; Space heating; Water heating;
Conference_Titel :
Neural Networks, 1995. Proceedings., IEEE International Conference on
Conference_Location :
Perth, WA
Print_ISBN :
0-7803-2768-3
DOI :
10.1109/ICNN.1995.487534
