Abstract
Load forecasting is very essential to the operation of electricity companies. It enhances the energy-efficient and reliable operation of a power system. This study shows Electricity Load Forecasting modeling based on Framelet Neural Network Technique (FNN) for Baghdad City. Framelet technique is implemented to the time series data, decomposing the data into number of Framelet coefficient signals. The decomposed signals are then fed into neural network for training. To obtain the predict forecast, the outputs from the neural network are recombined using the same Framelet technique. The simulation results showed that the model was capable of producing a reasonable forecasting accuracy in short term load forecast.
Highlights
Many power systems are being pushed to their limits to meet their customers’ demands, and spend a lot of resources in their operation scheduling
Price is only used with load to form a particular time-series pattern as the inputs for training the neural networks, the model output was only the predicated electricity load and tested with a number of different (a) values of hidden neurons, no significant changes are observed with the predicted results
Step two: As depicted in forecast model, three neural networks were created for the forecast model-one for the approximation series and one for each of the two Framelet coefficient series
Summary
Many power systems are being pushed to their limits to meet their customers’ demands, and spend a lot of resources in their operation scheduling. The solution to that is Neural Network as supervised models have been used to deal with the nonlinearity and non-stationary in electricity load prediction and have produced good and satisfactory results[1,3,5], for their approximation ability for nonlinear mapping and generalization. It suffers from the problem of obtaining monolithic global models for a time series. They have been used effectively for image compression, noise removal, object detection and large-scale structure analysis, among other applications[5]
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