Decision support in power systems based on load forecasting models and influence analysis of climatic and socio-economic factors
Wavelet Applications in Industrial Processing IV
This paper presents a decision support system for power load forecast and the learning of influence patterns of the socioeconomic and climatic factors on the power consumption based on mathematical and computational intelligenge methods, with the purpose of defining the future power consumption of a given region, as well as to provide a mean for the analysis of correlations between the power consumption and these factors. Here we use a linear modelo of regression for the forecasting, also
... ting a comparative analysis with neural networks, to prove its efectiveness; and also Bayesian networks for the learning of causal relationships from the data. It is possible, from the prospections (inferences) over the Bayesian networks, to present the users with many scenarios which can promote variations in the power consumption, given the climatic and socio-economic conditions of the state of Pará. This analysis can considerably aid in the analysis process of the power demands, what can lead to an anticipated decision making and, consequentially, a reduction in the operation costs of the system. EVALUATION OF THE OBTAINED RESULTS The evaluation of the results obtained by the application of the mathematical and computational methods was made considering two aspects: load forecast with regression methods and the analysis and visualization of the dependencies. Load forecast with regression methods Here the prospection studies were made in order to estimate the power consumption values. As it was previously specified on section 2, a prior study was made for the year of 2005 based on the data form Jan/91 to Dec/04. The result achieved by the estimation presented an error of approximately 1,47%, a value considered not only acceptable, but also inferior to all of the statistical methods used by the national power suppliers, which runs around 4%. This reduction represents, evidently, a considerable economy for energy purchase in a future market. As a comparative study, a load forecasting analysis using neural networks  and Kalman filters  were made. The neural network used in this study was a feed-forward multi-layer network with two hidden layers and using as learning algorithm the adaptative backpropagation. The same data of the annual consumption series supplied as input for the regression analysis, as described in section 2, were also submitted to the neural network and the Kalman filter. The results obtained for the regression methods, neural network and Kalman filter were of 1.47% 4.08% and 5.08% respectively.