We are developing a forecaster for daily extremes of demand for electric power encountered in the service area of a large midwest(cid:173) ern utility and using this application as a testbed for approaches to input dimension reduction and decomposition of network train(cid:173) ing. Projection pursuit regression representations and the ability of algorithms like SIR to quickly find reasonable weighting vectors enable us to confront the vexing architecture selection problem by reducing high-dimensional gradient searchs to fitting single-input single-output (SISO) subnets. We introduce dimension reduction algorithms, to select features or relevant subsets of a set of many variables, based on minimizing an index of level-set dispersions (closely related to a projection index and to SIR), and combine them with backfitting to implement a neural network version of projection pursuit. The performance achieved by our approach, when trained on 1989, 1990 data and tested on 1991 data, is com(cid:173) parable to that achieved in our earlier study of backpropagation trained networks.

Demand forecasting is one of the most important aspects of logistics. While some businesses are able to make educated guesses based on previous years' sales, demand forecasting using artificial intelligence (AI) technology can help companies achieve higher degrees of precision when predicting future demand for their products. But how AI-Enabled demand forecasting boosts logistics? Forecasting is a complex task that can be made simpler by using Artificial Intelligence (AI) to analyze historical data about orders placed, the market, shipping routes, and weather. Today, demand forecasting has evolved into what is known as predictive demand planning or forecasting.

Demand forecasting is an important challenge for industries seeking to optimize service quality and expenditures. Generating accurate forecasts is difficult because it depends on the quality of the data available to train predictive models, as well as on the model chosen for the task. We evaluate the approach on two datasets of varying complexity and compare the results with three machine learning algorithms. Results show our approach can outperform these approaches.

Our efforts proceed in the context of a problem suggested by the operational needs of a particular electric utility to make daily forecasts of short-term load or demand. Forecasts are made at midday (1 p.m.) on a weekday t ( Monday - Thursday), for the next evening peak e(t) (occuring usually about 8 p.m. in the winter), the daily minimum d(t

Our efforts proceed in the context of a problem suggested by the operational needs of a particular electric utility to make daily forecasts of short-term load or demand. Forecasts are made at midday (1 p.m.) on a weekday t ( Monday - Thursday), for the next evening peak e(t) (occuring usually about 8 p.m. in the winter), the daily minimum d(t

Our efforts proceed in the context of a problem suggested by the operational needs of a particular electric utility to make daily forecasts of short-term load or demand. Forecasts are made at midday (1 p.m.) on a weekday t ( Monday - Thursday), for the next evening peak e(t) (occuring usually about 8 p.m. in the winter), the daily minimum d(t