A Data-Driven Approach to Forecasting Heating and Cooling Energy Demand in an Office Building as an Alternative to Multi-Zone Dynamic Simulation

被引:5
|
作者
Godinho, Xavier [1 ,2 ]
Bernardo, Hermano [1 ,2 ]
de Sousa, Joao C. [1 ,2 ]
Oliveira, Filipe T. [1 ,2 ]
机构
[1] Polytech Leiria, Sch Technol & Management, P-2411901 Leiria, Portugal
[2] Univ Coimbra, Polo 2, DEEC, INESC Coimbra, P-3030790 Coimbra, Portugal
来源
APPLIED SCIENCES-BASEL | 2021年 / 11卷 / 04期
关键词
load forecasting; building energy demand; artificial neural networks; support vector machines; simulated annealing; data-driven methods; multi-zone dynamic simulation;
D O I
10.3390/app11041356
中图分类号
O6 [化学];
学科分类号
0703 ;
摘要
Nowadays, as more data is now available from an increasing number of installed sensors, load forecasting applied to buildings is being increasingly explored. The amount and quality of resulting information can provide inputs for smarter decisions when managing and operating office buildings. In this article, the authors use two data-driven methods (artificial neural networks and support vector machines) to predict the heating and cooling energy demand in an office building located in Lisbon, Portugal. In the present case-study, these methods prove to be an accurate and appealing alternative to the use of accurate but time-consuming multi-zone dynamic simulation tools, which strongly depend on several parameters to be inserted and user expertise to calibrate the model. Artificial neural networks and support vector machines were developed and parametrized using historical data and different sets of exogenous variables to encounter the best performance combinations for both the heating and cooling periods of a year. In the case of support vector regression, a variation introduced simulated annealing to guide the search for different combinations of hyperparameters. After a feature selection stage for each individual method, the results for the different methods were compared, based on error metrics and distributions. The outputs of the study include the most suitable methodology for each season, and also the features (historical load records, but also exogenous features such as outdoor temperature, relative humidity or occupancy profile) that led to the most accurate models. Results clearly show there is a potential for faster, yet accurate machine-learning based forecasting methods to replace well-established, very accurate but time-consuming multi-zone dynamic simulation tools to forecast building energy consumption.
引用
收藏
页码:1 / 23
页数:23
相关论文
共 50 条
  • [21] Data visualization and analysis of energy flow on a multi-zone building scale
    Abdelalim, Aly
    O'Brien, William
    Shi, Zixiao
    AUTOMATION IN CONSTRUCTION, 2017, 84 : 258 - 273
  • [22] Towards scalable physically consistent neural networks: An application to data-driven multi-zone thermal building models
    Di Natale, L.
    Svetozarevic, B.
    Heer, P.
    Jones, C. N.
    APPLIED ENERGY, 2023, 340
  • [23] A Novel Hybrid Technique For Building Demand Forecasting Based On Data-driven And Urban Scale Simulation Approaches
    Tardioli, Giovanni
    Kerrigan, Ruth
    Oates, Michael
    O'Donnell, James
    Finn, Donal P.
    PROCEEDINGS OF BUILDING SIMULATION 2019: 16TH CONFERENCE OF IBPSA, 2020, : 3722 - 3729
  • [24] Sensitivity analysis of data-driven building energy demand forecasts
    Bunning, Felix
    Heer, Philipp
    Smith, Roy S.
    Lygeros, John
    CLIMATE RESILIENT CITIES - ENERGY EFFICIENCY & RENEWABLES IN THE DIGITAL ERA (CISBAT 2019), 2019, 1343
  • [25] A Data-Driven Dynamic Programming Model for Research Position Demand Forecasting
    Xie Y.
    Wu D.
    Chen Y.
    Jiao W.
    Li J.
    Annals of Data Science, 2017, 4 (01) : 19 - 30
  • [26] Ten questions concerning data-driven modelling and forecasting of operational energy demand at building and urban scale
    Kazmi, Hussain
    Fu, Chun
    Miller, Clayton
    BUILDING AND ENVIRONMENT, 2023, 239
  • [27] Data-driven predictive models for residential building energy use based on the segregation of heating and cooling days
    Kamel, Ehsan
    Sheikh, Shaya
    Huang, Xueqing
    ENERGY, 2020, 206
  • [28] Energy modelling and control of building heating and cooling systems with data-driven and hybrid models-A review
    Balali, Yasaman
    Chong, Adrian
    Busch, Andrew
    O'Keefe, Steven
    RENEWABLE & SUSTAINABLE ENERGY REVIEWS, 2023, 183
  • [29] Dynamic simulation of a solar heating and cooling system for an office building located in Southern Italy
    Angrisani, Giovanni
    Entchev, Evgueniy
    Roselli, Carlo
    Sasso, Maurizio
    Tariello, Francesco
    Yaici, Wahiba
    APPLIED THERMAL ENGINEERING, 2016, 103 : 377 - 390
  • [30] Modeling and forecasting building energy consumption: A review of data-driven techniques
    Bourdeau, Mathieu
    Zhai, Xiao Qiang
    Nefzaoui, Elyes
    Guo, Xiaofeng
    Chatellier, Patrice
    SUSTAINABLE CITIES AND SOCIETY, 2019, 48