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: 107,347,330

 Building an intelligent seasonal time series model for forecasting building electricity load
Tác giả hoặc Nhóm tác giả: Thuy-Linh Le*, Ngoc Hoang Tran, Duy Vu Luu, Duc Sy Nguyen, Thi Thu Ha Truong, Le Nhat Hoang Tran , Thi Ai Lanh Nguyen
Nơi đăng: Tạp chí Khoa học và Công nghệ, Đại học Đà Nẵng; Số: Vol 20, NO 11.2;Từ->đến trang: 33-37;Năm: 2022
Lĩnh vực: Kỹ thuật; Loại: Bài báo khoa học; Thể loại: Trong nước
TÓM TẮT
The building sector is a significant energy consumer, and its share of energy consumption is increasing because of urbanization. Forecasting the electricity load for improving building energy efficiency is imperative for reducing energy costs and environmental impacts. This study first builds a seasonal time-series model, then integrates it with IoT in the energy-predict systems. Notably, the built time-series model gives positive results with an R2 training of 0.814 and an R2 test of 0.803, which are much better than the regression model in accuracy and feature cost. Lastly, the proposed system automatically collects data from an IoT platform, predicts energy consumption, and sends results to end users. This system can help the user control their energy consumption or abnormal energy consumption in a home in real time.
ABSTRACT
The building sector is a significant energy consumer, and its share of energy consumption is increasing because of urbanization. Forecasting the electricity load for improving building energy efficiency is imperative for reducing energy costs and environmental impacts. This study first builds a seasonal time-series model, then integrates it with IoT in the energy-predict systems. Notably, the built time-series model gives positive results with an R2 training of 0.814 and an R2 test of 0.803, which are much better than the regression model in accuracy and feature cost. Lastly, the proposed system automatically collects data from an IoT platform, predicts energy consumption, and sends results to end users. This system can help the user control their energy consumption or abnormal energy consumption in a home in real time.
© Đại học Đà Nẵng
 
 
Địa chỉ: 41 Lê Duẩn Thành phố Đà Nẵng
Điện thoại: (84) 0236 3822 041 ; Email: dhdn@ac.udn.vn