Parallel distributed processing: explorations in the microstructure of cognition, vol. 1: foundations
Introduction to Artificial Neural Systems
Introduction to Artificial Neural Systems
Neural Networks: A Comprehensive Foundation
Neural Networks: A Comprehensive Foundation
Neural Networks for Pattern Recognition
Neural Networks for Pattern Recognition
Acceleration Techniques for the Backpropagation Algorithm
Proceedings of the EURASIP Workshop 1990 on Neural Networks
ANNSTLF-a neural-network-based electric load forecasting system
IEEE Transactions on Neural Networks
Constructive neural-network learning algorithms for pattern classification
IEEE Transactions on Neural Networks
| Hi-index | 0.00 |
This paper describes a research where the main goal was to predict the future values of a time series of the hourly demand of Portugal global electricity consumption in the following day. In a preliminary phase several regression techniques were experimented: K Nearest Neighbors, Multiple Linear Regression, Projection Pursuit Regression, Regression Trees, Multivariate Adaptive Regression Splines and Artificial Neural Networks (ANN). Having the best results been achieved with ANN, this technique was selected as the primary tool for the load forecasting process. The prediction for holidays and days following holidays is analyzed and dealt with. Temperature significance on consumption level is also studied. Results attained support the adopted approach.