Adaptive Neural Network Prediction Model for Energy Consumption
This paper discusses on the adaptive neural network model for predicting the energy consumption at a metering station. The function of the metering system is to calculate the energy consumption of the outgoing gas flow. To ensure the robustness of the developed model, it is suggested to make the model an adaptive model that will periodically update the weights. This will ensure the reliability of the model. A dynamic prediction model that can adapt itself to changes in the energy consumption pattern is desirable especially for short-term energy prediction. It is also important for an online running of the metering system. Two methods of weights update are proposed and tested, namely the accumulative training and sliding window training. The developed adaptive neural network model is then compared with the static neural network. Adaptive neural network for energy consumption has shown better result and recommended for implementation in the metering station.
accumulative training method adaptive neural network metering system-gliding window training method
Maryam Jamela Ismail Rosdiazli Ibrahim Idris Ismail
Electrical and Electronics Engineering Department,University Technologi Petronas,Tronoh,Perak,Malaysia
国际会议
上海
英文
109-113
2011-03-11（万方平台首次上网日期，不代表论文的发表时间）