We evaluate statistical and machine learning methods for half-hourly 1-step-ahead electricity demand prediction using Australian electricity data. We show that the machine learning methods, that use autocorrelation feature selection and Backpropagation Neural Networks, Linear Regression and Support Vector Regression as prediction algorithms, outperform the statistical methods Exponential Smooth...

An artificial neural network (ANN) model for the prediction of glucose concentration in a glucose-insulin regulation system for type 1 diabetes mellitus is developed and validated by using the Continuous Glucose Monitoring System (CGMS) data. This network consists of structured framework according to the compartmental structure of the Hovorka-Wilinska model (HWM), and an additional update schem...

electricity price predictions have become a major discussion on competitive market under deregulated power system. but, the exclusive characteristics of electricity price such as non-linearity, non-stationary and time-varying volatility structure present several challenges for this task. in this paper, a new forecast strategy based on the iterative neural network is proposed for day-ahead price...

In this paper we present the application of techniques for Correlation Dimension Estimation to estimate the model order of electrical load data. Based on a correct model order, appropriately structured neural nets for load forecasting were designed. Satisfactory results were obtained in one-hour-ahead electrical load prediction on a six months benchmark from an electric utility in the USA.

© 2012 Filho et al., licensee InTech. This is an open access chapter distributed under the terms of the Creative Commons Attribution License (http://creativecommons.org/licenses/by/3.0), which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited. Short-Term Energy Price Prediction Multi-Step-Ahead in the Brazilian Market Using Data...