A Knowledge Based System for Medium Term Load Forecasting

The paper reports a new methodology for the medium term load forecasting providing monthly energy consumption and monthly maximum demand for a municipal utility. To this aim a modular procedure, based on an artificial neural network (ANN), which is a multi-layer perceptron using a back-propagation f...

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Bibliographic Details
Main Authors: Falvo, M.C., Lamedica, R., Pierazzo, S., Prudenzi, A.
Format: Conference Proceeding
Language:English
Subjects:
Artificial neural networks
Demand forecasting
Energy consumption
Knowledge based systems
Load forecasting
Multilayer perceptrons
Power generation
Power system modeling
Power system planning
Statistical analysis
Online Access:Request full text
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Summary:The paper reports a new methodology for the medium term load forecasting providing monthly energy consumption and monthly maximum demand for a municipal utility. To this aim a modular procedure, based on an artificial neural network (ANN), which is a multi-layer perceptron using a back-propagation feed-forward algorithm, is implemented. The monthly forecasts are obtained through some knowledge based activities from the output of stage providing annual energy forecast. The choice of the prediction stage is reported by illustrating the results of a comparison with canonical statistical methods, such as exponential smoothing and ARIMA. The whole knowledge based procedure is illustrated in due detail and some best forecasting performances are reported thus demonstrating validity of the proposed approach
ISSN:2160-8555
2160-8563
DOI:10.1109/TDC.2006.1668697