Input Window Size and Neural Network Predictors
IJCNN '00 Proceedings of the IEEE-INNS-ENNS International Joint Conference on Neural Networks (IJCNN'00)-Volume 2 - Volume 2
Forecasting of the electric energy demand trend and monthly fluctuation with neural networks
Computers and Industrial Engineering
Computers and Industrial Engineering
GA-based neural network for energy recovery system of the electric motorcycle
Expert Systems with Applications: An International Journal
Temporal association rules mining: a heuristic methodology applied to time series databases (TSDBs)
CIMMACS '10 Proceedings of the 9th WSEAS international conference on computational intelligence, man-machine systems and cybernetics
Forecasting medical cost inflation rates: A model comparison approach
Decision Support Systems
Mining association rules from time series to explain failures in a hot-dip galvanizing steel line
Computers and Industrial Engineering
Modeling and analyzing technology innovation in the energy sector: Patent-based HMM approach
Computers and Industrial Engineering
International Journal of Business Information Systems
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Neural networks have been widely used for short-term, and to a lesser degree medium and long-term, demand forecasting. In the majority of cases for the latter two applications, multivariate modeling was adopted, where the demand time series is related to other weather, socio-economic and demographic time series. Disadvantages of this approach include the fact that influential exogenous factors are difficult to determine, and accurate data for them may not be readily available. This paper uses univariate modeling of the monthly demand time series based only on data for 6years to forecast the demand for the seventh year. Both neural and abductive networks were used for modeling, and their performance was compared. A simple technique is described for removing the upward growth trend prior to modeling the demand time series to avoid problems associated with extrapolating beyond the data range used for training. Two modeling approaches were investigated and compared: iteratively using a single next-month forecaster, and employing 12 dedicated models to forecast the 12 individual months directly. Results indicate better performance by the first approach, with mean percentage error (MAPE) of the order of 3% for abductive networks. Performance is superior to nai@?ve forecasts based on persistence and seasonality, and is better than results quoted in the literature for several similar applications using multivariate abductive modeling, multiple regression, and univariate ARIMA analysis. Automatic selection of only the most relevant model inputs by the abductive learning algorithm provides better insight into the modeled process and allows constructing simpler neural network models with reduced data dimensionality and improved forecasting performance.