山东大学学报 (工学版) ›› 2022, Vol. 52 ›› Issue (1): 9-18.doi: 10.6040/j.issn.1672-3961.0.2021.308
孙鸿昌1,周风余1*,单明珠2,翟文文2,牛兰强2
SUN Hongchang1, ZHOU Fengyu1*, SHAN Mingzhu2, ZHAI Wenwen2, NIU Lanqiang2
摘要： 针对公共建筑能耗监测平台采集的空调能耗数据存在缺失、异常等问题,提出一种将无监督学习与监督学习结合的基于模式划分的空调能耗混合填补方法。利用k-means聚类算法将空调能耗数据划分至制冷、制热和独立新风3种运行模式中。在制冷及制热模式下,提出了一种BP神经网络(back propagation neural network, BPNN)和改进粒子群优化算法(ameliorate particle swarm optimization, APSO)相结合的混合填补策略,进行能耗填补,采用随机森林(random forest, RF)作为特征提取策略,用改进的惯性权重和速度更新方程的APSO优化BPNN的初始参数;在独立新风模式下,采用k最邻近算法(k-nearest neighbor, kNN)填补能耗。青岛市某商场空调能耗试验数据分析结果表明,与RF-APSO-BPNN算法、BPNN算法、小脑神经网络算法(cerebellar model articulation controller, CMAC)相比,本研究方法填补空调能耗的平均百分比误差分别减少了53.44%、69.39%、62.15%。RF-APSO-BPNN-kNN混合方法填补空调能耗更优于其他算法。
中图分类号:
| [1] 丁勇,吕婕. 公共建筑能耗与能效披露制度试点问题与建议[J]. 暖通空调, 2021, 51(8): 70-77. DING Y, LÜ J. Problems and suggestions on pilot of building energy consumption and energy efficiency disclosure systems [J]. Journal of HV&AC, 2021, 51(8): 70-77. [2] 梁金涵,章文杰,林常青,等. 新型城镇化背景下中国建筑能耗总量分析与政策建议[J]. 制冷与空调, 2020, 34(2): 175-184. LIANG J H, ZHANG W J, LIN C Q, et al. Analysis of China's total building energy consumption and policy proposal under the background of new urbanization[J]. Refrigeration and Air Conditioning, 2020, 34(2): 175-184. [3] WANG T H. Research on influencing factors and prediction model of public building energy consumption in colleges and universities[D]. Xi'an: Xi'an University of Architecture and Technology, 2018. [4] FAN C, XIAO F, MADSEN H, et al. Temporal knowledge discovery in big BAS data for building energy management[J]. Energy and Buildings, 2015, 109: 75-89. [5] FAN C, XIAO F, YAN C C. A framework for knowledge discovery in massive building automation data and its application in building diagnostics[J]. Automation in Construction, 2015, 50: 81-90. [6] MOR G, CIPRIANO J, MARTIRANO G, et al. A data-driven method for unsupervised electricity consumption characterisation at the district level and beyond[J]. Energy Reports, 2021, 7: 5667-5684. [7] 李梅香,彭惠旺,陈毅兴,等. 商场建筑运行能耗实测数据修复方法研究[J]. 建筑节能, 2021, 49(5): 37-45. LI M X, PENG H W, CHEN Y X, et al. A novel method of shopping mall energy consumption data restoration[J]. Journal of Building Energy Efficiency, 2021, 49(5): 37-45. [8] 周璇,崔少伟,周裕东. 办公建筑逐时能耗异常数据在线插补方法[J]. 建筑科学, 2018, 34(6): 82-90. ZHOU X, CUI S W, ZHOU Y D. Online interpolation methods for abnormal data of hourly energy consumption of office buildings[J]. Building Science, 2018, 34(6): 82-90. [9] SHAO M L, WANG X, BU Z, et al. Prediction of energy consumption in hotel buildings via support vector machines[J]. Sustainable Cities and Society, 2020, 57: 102-128. [10] 周优,陈义波,谭洪卫,等. 公共建筑电耗监测数据缺失及异常的预处理方法探究[J]. 建筑技术, 2018, 49(5): 469-472. ZHOU Y, CHEN Y B, TAN H W, et al. Study on preprocessing methods of missing and abnormal monitored electricity consumed in public buildings[J]. Architecture Technology, 2018, 49(5): 469-472. [11] 章挺飞, 罗恒, 刘杭. 基于LSTM网络的建筑能耗预测方法[J]. 苏州科技大学学报(自然科学版), 2020, 37(4): 78-84. ZHANG T F, LUO H, LIU H. Prediction of building energy consumption based on LSTM[J]. Journal of Suzhou University of Science and Technology(Natural Science), 2020, 37(4): 78-84. [12] KAPETANAKIS D S, MANGINA E, FINN D P. Input variable selection for thermal load predictive models of commercial buildings[J]. Energy and Buildings, 2017, 137: 13-26. [13] DING Y, ZHANG Q, YUAN T H, et al. Effect of input variables on cooling load prediction accuracy of an office building[J]. Applied Thermal Engineering, 2018, 128: 225-234. [14] BIAN H, ZHONG Y, SUN J, et al. Study on power consumption load forecast based on K-means clustering and FCM-BP model[J]. Energy Reports, 2020, 6:693-700. [15] ROSTAM M G, SADATINEJAD S J, MALEKIAN A. Precipitation forecasting by large-scale climate indices and machine learning techniques[J]. Journal of Arid Land, 2020, 12(5): 854-864. [16] 张雪嫣,黎晓东,孙洁香. 基于随机森林的烟草烘丝设备参数数据相关性分析研究[J]. 制造业自动化, 2019, 41(8): 145-148. ZHANG X Y, LI X D, SUN J X. Correlation analysis of parameter data of tobacco drying equipment based on random forest[J]. Manufacturing Automation, 2019, 41(8): 145-148. [17] YU M, YANG J Q, LU Y, et al. BP-ANN model coupled with particle swarm optimization for the efficient prediction of 2-chlorophenol removal in an electro-oxidation system[J]. International Journal of Environmental Research and Public Health, 2019, 16(14): 24-54. [18] HEFNY H A, AZAB S S. Chaotic particle swarm optimization[C] //2010 the 7th International Conference on Informatics and Systems(INFOS). Cairo, Egypt: IEEE, 2010: 1-8. [19] 李洪奇, 杨中国, 朱丽萍, 等. 基于数据集特征的KNN最优K值预测方法[J]. 计算机应用与软件, 2016, 33(6): 54-58. LI H Q, YANG Z G, ZHU L P, et al. Prediction method of optimal K value in KNN based on dataset features[J]. Computer Applications and Software, 2016, 33(6): 54-58. [20] 谢霖铨,赵楠,徐浩,等. 基于属性相关性的KNN近邻填补算法改进[J]. 江西理工大学学报, 2019, 40(1): 95-101. XIE L Q, ZHAO N, XU H, et al. KNN nearest neighbor filling algorithm based on attribute correlation[J]. Journal of Jiangxi University of Science and Technology, 2019, 40(1): 95-101. [21] MARTINO A, GHIGLIETTI A, IEVA F, et al. A k-means procedure based on a Mahalanobis type distance for clustering multivariate functional data[J]. Statistical Methods and Applications, 2019, 28: 301-322. |
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