MARC 主機 00000nam  2200409   4500 
001    AAI1496704 
005    20121027121057.5 
008    121027s2011    ||||||||||||||||| ||eng d 
020    9781124771717 
035    (UMI)AAI1496704 
040    UMI|cUMI 
100 1  Sakauchi, Tsuginosuke 
245 10 Applying Bayesian forecasting to predict new customers' 
       heating oil demand 
300    137 p 
500    Source: Masters Abstracts International, Volume: 50-01, 
       page: 0477 
500    Adviser: George Corliss 
502    Thesis (M.S.)--Marquette University, 2011 
520    This thesis presents a new forecasting technique that 
       estimates energy demand by applying a Bayesian approach to
       forecasting. We introduce our Bayesian Heating Oil 
       Forecaster (BHOF), which forecasts daily heating oil 
       demand for individual customers who are enrolled in an 
       automatic delivery service provided by a heating oil sales
       and distribution company. The existing forecasting method 
       is based on linear regression, and its performance 
       diminishes for new customers who lack historical delivery 
       data. Bayesian methods, on the other hand, respond 
       effectively in the start-up situation where no prior data 
       history is available 
520    Our Bayesian Heating Oil Forecaster uses forecasters' past
       performances for existing customers to adjust the current 
       forecast for target customers. We adapted a Bayesian 
       approach to forecasting combined with domain knowledge and
       original ideas to develop our Bayesian Heating Oil 
       Forecaster, which forecasts demand for target customers 
       without relying on their historical deliveries 
520    Performance evaluation demonstrates that our Bayesian 
       Heating Oil Forecaster shows increased performance over 
       the existing forecasting method when the two techniques 
       are combined. We used Root Mean Squared Error (RMSE) and 
       Mean Absolute Percent Error (MAPE) to compare the 
       performance of the two algorithms. Compared to the 
       existing forecasting method alone, our Simple Average 
       model, which combines the forecasts from the existing 
       forecasting method and our Bayesian Heating Oil Forecaster,
       recorded an overall improvement of 2.4% in RMSE, 5.0% in 
       MAPE Actual, and 2.8% in MAPE Capacity for company A and 
       0.3%, 7.1%, and 2.8% for company B 
590    School code: 0116 
650  4 Statistics 
650  4 Energy 
650  4 Operations Research 
690    0463 
690    0791 
690    0796 
710 2  Marquette University.|bElectrical & Computer Engineering 
773 0  |tMasters Abstracts International|g50-01 
856 40 |u