Thông tin chung
  English
  Đề tài NC khoa học
  Bài báo, báo cáo khoa học
  Hướng dẫn Sau đại học
  Sách và giáo trình
  Các học phần và môn giảng dạy
  Giải thưởng khoa học, Phát minh, sáng chế
  Khen thưởng
  Thông tin khác
  Tài liệu tham khảo
  Hiệu chỉnh
 
Số người truy cập: 107,504,175

 Predicting energy consumption in multiple buildings using machine learning for improving energy efficiency and sustainability
Tác giả hoặc Nhóm tác giả: Anh-Duc Pham, Ngoc-Tri Ngo, Thi Thu Ha Truong, Nhat-To Huynh, Ngoc-Son Truong
Nơi đăng: Journal of Cleaner Production; Số: 260;Từ->đến trang: 121082;Năm: 2020
Lĩnh vực: Kỹ thuật; Loại: Bài báo khoa học; Thể loại: Quốc tế
TÓM TẮT
Buildings must be energy efficient and sustainable because buildings have contributed significantly to world energy consumption and greenhouse gas emission. Predicting energy consumption patterns in buildings is beneficial to utility companies, users, and facility managers because it can help to improve energy efficiency. This work proposed a Random Forests (RF) – based prediction model to predict the short-term energy consumption in the hourly resolution in multiple buildings. Five one-year datasets of hourly building energy consumption were used to examine the effectiveness of the RF model throughout the training and test phases.
ABSTRACT
Buildings must be energy efficient and sustainable because buildings have contributed significantly to world energy consumption and greenhouse gas emission. Predicting energy consumption patterns in buildings is beneficial to utility companies, users, and facility managers because it can help to improve energy efficiency. This work proposed a Random Forests (RF) – based prediction model to predict the short-term energy consumption in the hourly resolution in multiple buildings. Five one-year datasets of hourly building energy consumption were used to examine the effectiveness of the RF model throughout the training and test phases.
© Đại học Đà Nẵng
 
 
Địa chỉ: 41 Lê Duẩn Thành phố Đà Nẵng
Điện thoại: (84) 0236 3822 041 ; Email: dhdn@ac.udn.vn