Forecasting short-term mortality trends using Bernstein polynomials

Efficient Short-Term Electricity Load Forecasting Using Recurrent Neural Networks

Short term load forecasting (STLF) plays an important role in the economic and reliable operation ofpower systems. Electric load demand has a complex profile with many multivariable and nonlineardependencies. In this study, recurrent neural network (RNN) architecture is presented for STLF. Theproposed model is capable of forecasting next 24-hour load profile. The main feature in this networkis ...

Short term load forecasting using fuzzy logic

Load forecasting is essential for planning and operation in energy management. It enhances the Energy efficient and reliable operation of a power system. The energy supplied by utilities meets the load plus the energy lost in the system is ensured by this tool. Since in power system the next day’s power generation must be scheduled every day. The dayahead short term load forecasting (STLF) is a...

Short-Term Load Forecasting Using Random Forests

This study proposes using a random forest model for short-term electricity load forecasting. This is an ensemble learning method that generates many regression trees (CART) and aggregates their results. The model operates on patterns of the time series seasonal cycles which simplifies the forecasting problem especially when a time series exhibits nonstationarity, heteroscedasticity, trend and m...

Short - Term Load Forecasting

This paper presents a novel hybrid method for short-term load forecasting. The system comprises of two artificial neural networks (ANN), assembled in a hierarchical order. The first ANN is a multilayer perceptron (MLP) which functions as integrated load predictor (ILP) for the forecasting day. The output of the ILP is then fed to another, more complex MLP, which acts as an hourly load predictor...

Short-Term Weather Forecasting