4.7 Article

Analysis and prediction of carbon emission in the large green commercial building: A case study in Dalian, China

相关参考文献

注意:仅列出部分参考文献,下载原文获取全部文献信息。
Article Energy & Fuels

Short- and Very Short-Term Firm-Level Load Forecasting for Warehouses: A Comparison of Machine Learning and Deep Learning Models

Andrea Maria N. C. Ribeiro et al.

Summary: This study investigates various models for predicting energy consumption in warehouses, including machine learning and deep learning methods. The results show that the XGBoost model performs best for short-term energy consumption forecasting, while the ARIMA model performs the worst.

ENERGIES (2022)

Article Green & Sustainable Science & Technology

Building Carbon Emission Scenario Prediction Using STIRPAT and GA-BP Neural Network Model

Sensen Zhang et al.

Summary: This paper studies the influencing factors of carbon emissions in the construction industry in Jiangsu Province and predicts the future carbon emissions trend. The research found that factors such as resident population, urbanization rate, and steel production have a catalytic effect on carbon emissions, while per capita GDP and added value of tertiary industry have a suppressive effect. The prediction results show that future carbon emissions in Jiangsu Province will decrease.

SUSTAINABILITY (2022)

Article Thermodynamics

Data mining algorithm and framework for identifying HVAC control strategies in large commercial buildings

Zhe Chen et al.

Summary: The HVAC control strategies in commercial buildings are often arbitrarily set by operators lacking professional skills. However, through data mining analysis, these strategies can be identified to improve performance and detect issues.

BUILDING SIMULATION (2021)

Article Thermodynamics

Diagnosis of building energy consumption in the 2012 CBECS data using heterogeneous effect of energy variables: A recursive partitioning approach

Doowon Choi et al.

Summary: This study applies the MOB algorithm to analyze the 2012 CBECS survey data, revealing the heterogeneous effects and mutual influences of energy variables on building energy consumption.

BUILDING SIMULATION (2021)

Article Computer Science, Information Systems

Development of an IoT-Driven Building Environment for Prediction of Electric Energy Consumption

Guneet Bedi et al.

IEEE INTERNET OF THINGS JOURNAL (2020)

Article Green & Sustainable Science & Technology

Energy consumption prediction and diagnosis of public buildings based on support vector machine learning: A case study in China

Yang Liu et al.

JOURNAL OF CLEANER PRODUCTION (2020)

Article Construction & Building Technology

Nonlinear autoregressive and random forest approaches to forecasting electricity load for utility energy management systems

Tanveer Ahmad et al.

SUSTAINABLE CITIES AND SOCIETY (2019)

Article Construction & Building Technology

Mixed-integer model predictive control of variable-speed heat pumps

Zachary Lee et al.

ENERGY AND BUILDINGS (2019)

Article Environmental Sciences

Life-cycle impacts of shower water waste heat recovery: case study of an installation at a university sport facility in the UK

Kenneth IP et al.

ENVIRONMENTAL SCIENCE AND POLLUTION RESEARCH (2018)

Article Construction & Building Technology

Development of building energy saving advisory: A data mining approach

Milad Ashouri et al.

ENERGY AND BUILDINGS (2018)

Article Engineering, Environmental

Modeling energy-related CO2 emissions from office buildings using general regression neural network

Hong Ye et al.

RESOURCES CONSERVATION AND RECYCLING (2018)

Review Green & Sustainable Science & Technology

Achieving zero emission in China's urban building sector: opportunities and barriers

Rui Xing et al.

CURRENT OPINION IN ENVIRONMENTAL SUSTAINABILITY (2018)

Article Green & Sustainable Science & Technology

A validated low carbon office building intervention model based on structural equation modelling

I. Blessing Mafimisebi et al.

JOURNAL OF CLEANER PRODUCTION (2018)

Article Green & Sustainable Science & Technology

Developing a CO2-e accounting method for quantification and analysis of embodied carbon in high-rise buildings

Vincent J. L. Gan et al.

JOURNAL OF CLEANER PRODUCTION (2017)

Article Energy & Fuels

Machine learning approaches for estimating commercial building energy consumption

Caleb Robinson et al.

APPLIED ENERGY (2017)

Article Construction & Building Technology

Forecasting diurnal cooling energy load for institutional buildings using Artificial Neural Networks

Chirag Deb et al.

ENERGY AND BUILDINGS (2016)

Article Construction & Building Technology

A socio-technical framework of zero-carbon building policies

Wei Pan et al.

BUILDING RESEARCH AND INFORMATION (2015)

Review Energy & Fuels

Thermal comfort and building energy consumption implications - A review

Liu Yang et al.

APPLIED ENERGY (2014)

Review Green & Sustainable Science & Technology

A review on applications of ANN and SVM for building electrical energy consumption forecasting

A. S. Ahmad et al.

RENEWABLE & SUSTAINABLE ENERGY REVIEWS (2014)

Review Green & Sustainable Science & Technology

State of the art in building modelling and energy performances prediction: A review

Aurelie Foucquier et al.

RENEWABLE & SUSTAINABLE ENERGY REVIEWS (2013)

Editorial Material Construction & Building Technology

Climate change, indoor environments, and health

John D. Spengler

INDOOR AIR (2012)

Article Engineering, Electrical & Electronic

Quantifying Changes in Building Electricity Use, With Application to Demand Response

Johanna L. Mathieu et al.

IEEE TRANSACTIONS ON SMART GRID (2011)

Article Computer Science, Artificial Intelligence

Top 10 algorithms in data mining

Xindong Wu et al.

KNOWLEDGE AND INFORMATION SYSTEMS (2008)

Article Construction & Building Technology

Applying support vector machines to predict building energy consumption in tropical region

B Dong et al.

ENERGY AND BUILDINGS (2005)

Article Construction & Building Technology

Debating the future of comfort: environmental sustainability, energy consumption and the indoor environment

H Chappells et al.

BUILDING RESEARCH AND INFORMATION (2005)