我國建築產業與住宅碳排量之佔比高達全國總碳排量之25%，故「建築節能」觀念之普及化與實地運用為降低能源使用與碳排放最直接的手段之一。本計畫提出「低耗能建築之智慧能源技術開發」為總計畫，使用NEP-II最後一年計畫所建置完成之「低碳排智慧綠建築」為研發與驗證場域，規劃4個技術子計畫相互整合，以完成智慧能源系統開發最終目標及產出。以下簡述各子計畫之工作重點：I. 總計畫：低耗能建築之智慧能源技術開發建立一套再生能源效益最大化指標，依據即時氣象資訊及負載，調控再生能源使用模式，達到綠能效益最大化；藉由蒐集長時間低碳排綠建築運行數據，搭配氣象資料，針對不同用電模式與情境，評估最適之創、儲能系統容量。II. 子計畫一：低耗能建築之能源評估分析建立低碳排智慧綠建築之空間熱能特徵模型，驗證實際量測值與模擬結果匹配性，可應用於未來不同規模尺度之低碳排智慧綠建築建置，及提供傳統高耗能建築之改良方向；並考慮人體舒適度，進而找出最佳室內配置。建置不同地區、環境參數資料庫。III. 子計畫二：智慧型電能管理專家系統之研製建立智慧型電能管理專家系統進行用電特徵分析及歸納，實現高效能之能源使用管理策略。並輔以相關控制流程管控電能，延長儲能系統之備援能力以及系統穩定度。根據建築供電系統孤島及併網運轉狀態提出管控策略。IV. 子計畫三：場域感測器系統於室內空間安裝多量微型感測器，透過分散式微型感測器蒐集空間狀態與使用者動態資料，進而分析使用者行為與空間條件。最終目標為「預測」使用者行為，對室內家電下達精確控制命令，以達節能與舒適度並存之目標。V. 子計畫四：光環境與智慧照明開發具智慧管理之節能照明技術，強調安全性、功能性、便利性及舒適性之智慧照明，結合指向性燈具設計並搭配全域微型感測資訊，以達區域照明智慧調控，進一步提升節能效益。本團隊執行「低耗能建築之智慧能源技術開發」計畫之3大核心步驟想法為：(1)示範場域模型實例驗證，(2)不同場域效益評估及系統優化，與(3)全域參數感測及智慧化控制。藉由與國內廠商之密切合作，將計畫研發成果擴散至產業，提升相關產業之競爭力。 ;The construction industries and buildings account for 25 % of carbon emission of Taiwan. It is, therefore, very important to promote the energy saving concept and technologies to the general publics. This proposal is aimed at developing smart energy technologies for low-energy-consumption buildings. We will use the low carbon emission smart building constructed in last year’s NEP-II project as the R&D and test site. This project consists of four subtasks. I. Overall：Smart energy technologies for low-energy-consumption buildings Establish an indicator for maximizing renewable energy utilization. According to load and weather condition, adjust renewable energy controlling mode to maximize its utilization. By collecting long term operation and weather data, recommend the most appropriate energy production and storage system to meet user needs. II. Subtask 1: Energy evaluation and analysis of low-energy-consumption buildings To establish a thermal model for low-energy-consumption buildings (LEB). Verify its accuracy with measured data. The model is then ready for use to simulate future LEB or to provide improvement recommendation of current non-LEB. It can also be used to calculate the predicted mean vote (PMV) and best furniture arrangement. We will build a parameter database for different locations and environment. III. Subtask 2: Development of smart electric energy management expert system We will build a smart electric energy management expert system to analyze electricity consumption characteristics. The expert system has wavelet feature capturing technique, fuzzy theory, and neurologic capabilities to optimize the electric energy system, either in grid-connected or stand-alone operation. IV. Subtask 3: Development of field micro-sensing system By deploying a large number of distributed micro-sensors, we will analyze indoor conditions with high space resolution. User behaviors can also be analyzed. These information can be used to predict user behavior and to efficiently control lighting and air-conditioning. V. Subtask 4: Smart lighting and lighting ambiences We will develop smart lighting technology with smart management. Using information obtained from Subtask 3, we will develop lighting strategy for mixed directional/non-directional LEDs to further improve energy efficiency for the lighting system. There are three main concepts of this project, including (1) location based efficiency and benefit assessment, and system optimization, (2) all-field sensing and smart control, and (3) validation by field test. By close cooperation with the industries, we can transfer the technology faster, and improve the competiveness of the industries.
