|
|
|
| Prediction of dynamic cooling and heating load considering growth characteristics of regional building |
Yuji DU1( ),Wei ZHONG1,*(),Huijin QIAN1,2,Zitao YU1 |
1. Polytechnic Institute, Zhejiang University, Hangzhou 310058, China
2. CECEP City Energy Conservation Limited Company, Changzhou 213000, China |
|
|
|
Abstract Load calculation theory, grey Verhulst model and equal dimensional innovation gray theory were combined to establish a comprehensive method and system for regional dynamic cooling and heating load prediction using MATLAB by considering the dynamic growth characteristics of regional buildings. Historical data of the building area in Changzhou high-speed railway new town from 2017 to 2022 were used to forecast the next ten-year heating and cooling loads in the region. Results show that the established three types of area prediction equations based on the equal dimensional innovation grey theory and Verhulst grey model achieve the first-class accuracy level. The heating and cooling loads in the High-speed Rail New City will experience rapid growth followed by slow growth until saturation in the next ten years. The saturation point is projected to be around 2030 with a saturated cooling load of 436 MW and a saturated heating load of 228 MW. The loads were reduced by approximately 7.52% and 19.86% respectively compared to the results obtained by the area index method (472 MW for cooling load and 285 MW for heating load).
|
|
Received: 24 February 2023
Published: 07 November 2023
|
|
|
| Fund: 国家重点研发计划资助项目（2019YFE0126000）；国家自然科学基金资助项目（51806190）；江苏省碳达峰碳中和科技创新专项资金（重大科技示范）资助项目（BE2022606） |
|
Corresponding Authors:
Wei ZHONG
E-mail: 11927120@zju.edu.cn;zhongw@zju.edu.cn
|
计及区域建筑群生长特性的动态冷热负荷预测
考虑区域建筑群的动态生长特性，结合负荷计算理论、灰色 Verhulst 模型及等维新息灰色理论，采用 MATLAB 构建完整的区域动态冷热负荷预测方法，以常州高铁新城 2017—2022年的建筑面积为历史数据，对区域未来10年的冷热负荷进行预测. 结果表明，采用基于等维新息灰色理论的 Verhulst 灰色模型建立的3种业态面积预测方程拟合精度均达到一级. 高铁新城未来10年冷热负荷先快速增长后缓慢增长直至饱和，达到饱和的时间约为 2030 年，冷、热负荷饱和规模分别为 436、228 MW，与采用面积指标法的计算结果（冷负荷为472 MW、热负荷为 285 MW）相比，分别降低了约 7.52%、19.86%.
关键词：
区域供冷供热系统,
动态负荷预测,
建筑群生长特性,
灰色模型,
新型城镇化
|
|
| [1] |
李笑竹, 王维庆 区域综合能源系统两阶段鲁棒博弈优化调度[J]. 浙江大学学报: 工学版, 2021, 55 (1): 177- 188
LI Xiaozhu, WANG Weiqing Bi-level robust game optimal scheduling of regional comprehensive energy system[J]. Journal of Zhejiang University: Engineering Science, 2021, 55 (1): 177- 188
|
|
|
| [2] |
XU Y, LI F, ASGARI A Prediction and optimization of heating and cooling loads in a residential building based on multi-layer perceptron neural network and different optimization algorithms[J]. Energy, 2022, 240 (C): 1- 12
|
|
|
| [3] |
ZHANG Y, JOHANSSON P, KALAGASIDIS A Assessment of district heating and cooling systems transition with respect to future changes in demand profiles and renewable energy supplies[J]. Energy Conversion and Management, 2022, 268 (9): 1- 21
|
|
|
| [4] |
叶存华. 规划阶段区域建筑群冷热负荷预测方法[D]. 天津: 天津大学, 2018.
YE Cunhua. Method of predicting cooling and heating load of regional buildings in planning stage [D]. Tianjin: Tianjin University, 2018.
|
|
|
| [5] |
SHAH R, PANDIT R K, GAUR M K. Role of building design in sustainable green development: a review [C]// 2nd International Symposium on Sustainable Development Research in the Asia-Pacific. Bangkok: Springer, 2019.
|
|
|
| [6] |
李鑫悦, 陈淑琴, 李鸿亮, 等 应用数据挖掘的高校教学建筑空调使用及其能耗分析[J]. 浙江大学学报: 工学版, 2020, 54 (9): 1677- 1688
LI Xinyue, CHEN Shuqin, LI Hongliang, et al Analysis of air-conditioning usage and energy consumption in campus teaching buildings with data mining[J]. Journal of Zhejiang University: Engineering Science, 2020, 54 (9): 1677- 1688
|
|
|
| [7] |
路作龙, 安爽, 杨文晓, 等 规划阶段建筑群冷热负荷预测方法研究[J]. 区域供热, 2021, (3): 37- 44
LU Zuolong, AN Shuang, YANG Wenxiao, et al Research on methods of cooling and heating load prediction for district buildings during urban planning stage[J]. District Heating, 2021, (3): 37- 44
|
|
|
| [8] |
KOSCHWITZ D, FRISCH J, TREECK C V Data-driven heating and cooling load predictions for non-residential buildings based on support vector machine regression and NARX recurrent neural network: a comparative study on district scale[J]. Energy, 2018, 165 (PT.A): 134- 142
|
|
|
| [9] |
欧科敏. 区域建筑群冷热负荷预测方法研究[D]. 长沙: 湖南大学, 2014.
OU Kemin. Research on methods of cooling and heating load prediction for district buildings [D]. Changsha: Hunan University, 2014.
|
|
|
| [10] |
章超波, 刘永政, 李宏波, 等 基于加权残差聚类的建筑负荷预测区间估计[J]. 浙江大学学报: 工学版, 2022, 56 (5): 930- 937
ZHANG Chaobo, LIU Yongzheng, LI Hongbo, et al Weighted residual clustering-based building load prediction interval estimation[J]. Journal of Zhejiang University: Engineering Science, 2022, 56 (5): 930- 937
|
|
|
| [11] |
王利珍 基于蒙特卡罗模拟的区域建筑冷负荷预测模型[J]. 中南大学学报: 自然科学版, 2014, 45 (11): 4026- 4032
WANG Lizhen Regional building cooling load prediction model based on Monte Carlo simulation[J]. Journal of Central South University: Science and Technology, 2014, 45 (11): 4026- 4032
|
|
|
| [12] |
周宇昊, 贾文琦, 潘毅群, 等 基于最近邻搜索的区域建筑负荷预测方法[J]. 建筑科学, 2021, 37 (6): 28- 35
ZHOU Yuhao, JIA Wenqi, PAN Yiqun, et al Regional building load prediction method based on the nearest neighbor search[J]. Building Science, 2021, 37 (6): 28- 35
|
|
|
| [13] |
伍小亭, 王砚, 宋晨, 等 基于暖通专业视角的区域能源系统思考: 概念、规划、设计[J]. 暖通空调, 2019, 49 (1): 2- 24
WU Xiaoting, WANG Yan, SONG Chen, et al Thinking of district energy system based on HVAC professional perspective: concept, planning and design[J]. Heating Ventilating and Air Conditioning, 2019, 49 (1): 2- 24
|
|
|
| [14] |
LIU Tianjie, JIAO Wenling, TIAN Xinghao A framework for uncertainty and sensitivity analysis of district energy systems considering different parameter types[J]. Energy Reports, 2021, 11 (7): 6908- 6920
|
|
|
| [15] |
李杰, 王秀丽, 邵成成, 等 地区电网负荷预测的灰色Verhulst与系统动力学组合模型[J]. 西安交通大学学报, 2021, 55 (1): 68- 77
LI Jie, WANG Xiuli, SHAO Chengcheng, et al Grey Verhulst and system dynamics combined model for regional power grid load forecasting[J]. Journal of Xi'an Jiaotong University, 2021, 55 (1): 68- 77
|
|
|
| [16] |
郑招阳, 陈新明 基于灰色 Verhulst 模型的灌溉水利用系数预测研究: 以咸阳市为例[J]. 水利与建筑工程学报, 2022, 20 (4): 64- 71
ZHENG Zhaoyang, CHEN Xinming Prediction of irrigation water utilization coefficient based on grey Verhulst model: taking Xianyang city as an example[J]. Journal of Water Resources and Architectural Engineering, 2022, 20 (4): 64- 71
|
|
|
| [17] |
SZPAK D, TCHÓRZEWSKA-CIELAK B, PIETRUCHA-URBANIK K, et al A grey-system theory approach to assess the safety of gas-supply systems[J]. Energies, 2022, 15 (22): 1- 13
|
|
|
| [18] |
LI J, ZHOU A, LIAO Y, et al. Forest ecological diversity change prediction discrete dynamic model [EB/OL]. (2022-01-19). https://doi.org/10.1155/2022/4869363.
|
|
|
| [19] |
江伟, 卢阿威 基于Verhulst理论的电力负荷预测及太阳能接入研究[J]. 技术与市场, 2022, 29 (11): 22- 25
JIANG Wei, LU Awei Research on power load forecasting and solar energy access based on Verhulst theory[J]. Technology and Market, 2022, 29 (11): 22- 25
|
|
|
| [20] |
张保民, 张伟, 金旭, 等 基于TRNSYS的区域供冷系统冷负荷预测[J]. 东北电力大学学报, 2021, 41 (6): 105- 110
ZHANG Baomin, ZHANG Wei, JIN Xu, et al Cooling load prediction of regional cooling system based on TRNSYS[J]. Journal of Northeast Electric Power University, 2021, 41 (6): 105- 110
|
|
|
| [21] |
徐欣. 天津办公建筑冷热负荷特征及预测算法[D]. 天津: 天津大学, 2014.
XU Xin. Research on the load characteristic and load prediction of office buildings in Tianjin [D]. Tianjin: Tianjin University, 2014.
|
|
|
| [22] |
朱丽, 张吉强, 王飞雪, 等 规划阶段建筑冷热负荷预测与特性分析[J]. 中南大学学报: 自然科学版, 2020, 51 (10): 2969- 2977
ZHU Li, ZHANG Jiqiang, WANG Feixue, et al Prediction and characteristics analysis of building heating and cooling loads at planning phase[J]. Journal of Central South University: Science and Technology, 2020, 51 (10): 2969- 2977
|
|
|
|
Viewed |
|
|
|
Full text
|
|
|
|
|
Abstract
|
|
|
|
|
Cited |
|
|
|
|
| |
Shared |
|
|
|
|
| |
Discussed |
|
|
|
|
