Long Term Electricity Demand & Peak Power Load Forecasting Variables Identification & Selection

Long-Term Peak Demand Forecasting by Using Radial Basis Function Neural Networks

Prediction of peak loads in Iran up to year 2011 is discussed using the Radial Basis Function Networks (RBFNs). In this study, total system load forecast reflecting the current and future trends is carried out for global grid of Iran. Predictions were done for target years 2007 to 2011 respectively. Unlike short-term load forecasting, long-term load forecasting is mainly affected by economy...

Challenges in forecasting peak electricity demand

We want to forecast the peak electricity demand in a half-hour period in twenty years time. We have fifteen years of half-hourly electricity data, temperature data and some economic and demographic data. The location is South Australia: home to the most volatile electricity demand in the world.

Levenberg-Marquardt Recurrent Networks for Long- Term Electricity Peak Load Forecasting

Increasing electricity demand in Java-Madura-Bali, Indonesia, must be addressed appropriately to avoid blackout by determining accurate peak load forecasting. Econometric approach may not be sufficient to handle this problem due to limitation in modelling nonlinear interaction of factors involved. To overcome this problem, Elman and Jordan Recurrent Neural Network based on Levenberg-Marquardt l...

Australia's long-term electricity demand forecasting using deep neural networks

Accurate prediction of long-term electricity demand has a significant role in demand side management and electricity network planning and operation. Demand over-estimation results in over-investment in network assets, driving up the electricity prices, while demand underestimation may lead to under-investment resulting in unreliable and insecure electricity. In this manuscript, we apply deep ne...

Long Term Electricity Demand Forecasting with Multi-agent-Based Model