Please use this identifier to cite or link to this item:
|Title:||Real-time pricing related short-term load forecasting|
|Authors:||Chang, C.S. |
|Citation:||Chang, C.S.,Yi, Minjun (1998). Real-time pricing related short-term load forecasting. Proceedings of the International Conference on Energy Management and Power Delivery, EMPD 2 : 411-416. ScholarBank@NUS Repository.|
|Abstract:||The production cost of electricity is not constant over time. It is dependent on the instantaneous load being supplied, the available generation and the state of the network. Real-Time Pricing (RTP), which sets the electricity selling price approximately equal to marginal cost, is proposed as a potential method for ensuring overall economic rationality, and for limiting the demands required by all consumers at times of limited supply or emergency conditions. Any tariff change will influence customer's electricity consumption behaviors. Some customers will respond to the real-time pricing by modifying or rescheduling electricity usage. This makes the short-term load forecasting problem more complicate than before. By combining the power of supervised and unsupervised neural networks, this paper presents a new solution for RTP related short-term load forecasting problem. The simulation result on realistic load and weather data confirms the good performance of this load forecaster.|
|Source Title:||Proceedings of the International Conference on Energy Management and Power Delivery, EMPD|
|Appears in Collections:||Staff Publications|
Show full item record
Files in This Item:
There are no files associated with this item.
checked on Nov 3, 2018
Items in DSpace are protected by copyright, with all rights reserved, unless otherwise indicated.