應用灰色理論於辦公大樓租戶暨公共區域設施維修時數預測

Application of Grey Theory in the Forecast of Facility Maintenance Hours for Office Building Tenants and Public Areas

建築物的設施設備提供了辦公大樓的生活機能所需，其設施又可區分機房設施、辦公室內設施和公共區域設施，對於辦公室內與公共區域的設施設備異常需求維修是來自於報修的回應式服務，也是承租戶對該大樓技術服務滿意度重要的評量。因此本研究以個案辦公大樓為例，運用灰色理論對辦公室內與公共區域回應式設施設備工單報修進行探討，其目的是為提供未來相關辦公大樓業主、執行管理者、物業管理公司、機電公司作為決策評估預測模型之參考，根據研究結果，灰關聯分析探討六大類別設施報修件數重要度，依次順序為電力系統、建築系統、水系統、空調系統、消防系統、人力派遣。GM（1，N）是以維修時數為依變數，報修件數、出租面積、租戶人數為自變數，依2011年1月至12月共12筆數據資料進行單月預測，其驗證結果為GM（1，N）平均絕對誤差為6.41％、平均準確度93.59％；於2011年維修時數年度預測值彙整統計，GM（1，N）平均絕對誤差為1.25％，平均準確度98.75％，顯示皆具有高準確的預測能力。於2011年維修時數年度預測值彙整統計，GM（1，N）平均絕對誤差為1.25％，顯示皆具有高準確的預測能力。

This study took case office building as subject and explored the responsive work order repair request of facilities and equipment in offices and public areas by gray theory, with the purpose of providing for future related office building owners, executive managers, property management companies, mechanical and electrical companies as reference for deciding and assessing forecast model. Important conclusions of this study are summarized as follows according to the study findings:1. Grey Relational Analysis discusses the importance of facilities repair number of six categories, namely, power systems, building systems, water systems, air conditioning systems, fire systems and manpower dispatch in order. In terms of facilities maintenance importance are power systems, building systems, water systems, air conditioning systems, manpower dispatch and fire systems in order.2. GM (1,N) and regression method took maintenance hours as dependent variables and repair number, leased area and tenants number as independent variables and conducted single month forecast based on 12 data from January to December 2011. The mean absolute error and average accuracy of GM (1,N) from verification results were 6.41% and 93.59%; the mean absolute error and average accuracy of regression model were 4.66% and 95.34%, indicating that they have highly accurate forecast capability.