Prediction and Management of Building Energy Consumption Based on Building Environment Simulation Design Platform DeST and Meteorological Data Analysis Algorithm
DOI:
https://doi.org/10.13052/spee1048-5236.4328Keywords:
Carbon emissions, building energy consumption, internet of things, cloud platform, support vector machine algorithmAbstract
Currently, the carbon emissions of building energy consumption account for a significant portion of all carbon emissions. How to reduce carbon emissions to achieve carbon neutrality is an important current research direction. Therefore this research builds a predictive algorithm model for analyzing energy consumption data of meteorological buildings using DeST platform for energy saving and emission reduction to achieve carbon neutrality. The new model uses Internet of Things and cloud platform technology to build a simulation building platform, and uses the support vector machine algorithm in the analysis algorithm to vectorize building energy consumption data, which can achieve normalization processing of building energy consumption and meteorological data. By processing building energy consumption data, prediction of building energy consumption at the next moment can be achieved. The experimental results show that the precision and accuracy of the new algorithm are higher than genetic algorithm 1 and 0.15 respectively, and 0.6 and 0.07 higher than clustering analysis algorithm respectively. Therefore, applying this algorithm model to building energy consumption prediction can significantly improve the accuracy and precision of the algorithm.
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References
Frimpong E, Twumasi E. Prediction of unregulated energy usage in office buildings. International Journal of Building Pathology and Adaptation, 2022, 40(2): 269–282.
Moghadasi M, Ozgoli H A, Farhani F. Steam consumption prediction of a gas sweetening process with methyldiethanolamine solvent using machine learning approaches. International Journal of Energy Research, 2021, 45(1): 879–893.
Pandey K, Basu B, Karmakar S. An Efficient Decision-Making Approach for Short Term Indoor Room Temperature Forecasting in Smart Environment: Evidence from India. International Journal of Information Technology & Decision Making, 2021, 20(2): 733–774.
Zhao Q. A short-term prediction method of building energy consumption based on gradient progressive regression tree. International Journal of Global Energy Issues, 2022, 44(3):182–197.
Peng B, Zou H M, Bai P F, YY Feng. Building energy consumption prediction and energy control of large-scale shopping malls based on a noncentralized self-adaptive energy management control system. Energy Exploration & Exploitation, 2020, 39(5):1381–1393.
Lei L, Chen W, Wu B, Chen Chao, Liu Wei. A building energy consumption prediction model based on rough set theory and deep learning algorithms. Energy and buildings, 2021, 240(1):2–20.
Wenninger S, Kaymakci C, Wiethe C. Explainable long-term building energy consumption prediction using QLattice. Applied Energy, 2022, 308(8):3–16.
Sun J, Liu G, Sun B, Xiao, Gang. Light-stacking strengthened fusion based building energy consumption prediction framework via variable weight feature selection. Applied Energy, 2021, 303(10):5–19.
Chen Y, Berardi U, Zhang F. Day-ahead prediction of hourly subentry energy consumption in the building sector using pattern recognition algorithms. Energy, 2020,211(10):5–17.
Luo X J, Oyedele L O. Forecasting building energy consumption: Adaptive long-short term memory neural networks driven by genetic algorithm. Advanced Engineering Informatics, 2021, 50(3):357–376.
Singh M, Sharston R. A literature review of building energy simulation and computational fluid dynamics co-simulation strategies and its implications on the accuracy of energy predictions: Building Services Engineering Research & Technology, 2022, 43(1):113–138.
Maltais L G, Gosselin L. Predictability analysis of domestic hot water consumption with neural networks: From single units to large residential buildings. Energy, 2021, 229(1):2–15.
Jang J, Han J, Leigh S B. Prediction of heating energy consumption with operation pattern variables for non-residential buildings using LSTM networks. Energy and Buildings, 2022, 255(1):2–13.
Dong Z, Liu J, Liu B, Li K, Li X. Hourly energy consumption prediction of an office building based on ensemble learning and energy consumption pattern classification. Energy and Buildings, 2021, 241(2):2–15.
Fan B, Xing X. Intelligent Prediction Method of Building Energy Consumption Based on Deep Learning. Scientific programming, 2021, 21(14):2–9.
Fu H, Baltazar J C, Claridge D E. Review of developments in whole-building statistical energy consumption models for commercial buildings. Renewable and Sustainable Energy Reviews, 2021, 147(9):2–10.
Wang Y, Gillich A, Lu D, Saber E M, Yebiyo M, Kang R, Ford A, Hewitt M. Performance prediction and evaluation on the first balanced energy networks (BEN) part I: BEN and building internal factors. Energy, 2021, 221(15):2–24.
Ma L, Huang Y, Zhao T. A synchronous prediction method for hourly energy consumption of abnormal monitoring branch based on the data-driven. Energy and buildings, 2022, 260(4):2–14.
Gao L, Liu T, Cao T, Hwang Y, Radermacher R. Comparing deep learning models for multi energy vectors prediction on multiple types of building. Applied Energy, 2021, 301(1):2–25.
Usman A M, Abdullah M K. An Assessment of Building Energy Consumption Characteristics Using Analytical Energy and Carbon Footprint Assessment Model. Green and Low-Carbon Economy, 2023, 1(1): 28–40.
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