|
|
|
| Analysis and forecast of industrial power consumption based on cloud platform |
| Wang Hongfei1, Zhou Xin1, Xu Zhezhuang1, Zhang Shijie1, Xia Yuxiong2 |
1. School of Electrical Engineering and Automation, Fuzhou University, Fuzhou 350108;
2. Fujian Huatuo Automation Technology Co., Ltd, Fuzhou 350003 |
|
|
|
|
Abstract Power consumption analysis and forecasting are of great significance to the energy management of industrial enterprises. Existing industrial power analysis and prediction methods are mostly limited to off-line local calculations. Data import and export steps still require manual operations, and there are problems such as low efficiency and poor real-time performance. In response to this problem, based on the industrial electricity data acquired by an industrial enterprise electricity data collection system, this paper implements real-time data import on the alibaba cloud platform, and analyzes the main factors affecting the industrial electricity consumption, and then uses the gradient to enhance The decision tree regression algorithm constructed the industrial electricity prediction model of the industrial enterprise, and compared with the support vector regression machine and linear regression algorithm, which proved that the gradient lifting decision tree regression algorithm has better prediction effect. On the other hand, this paper also makes a statistical analysis of the data synchronization time and algorithm calculation time of the cloud platform, which proves that the industrial power analysis and prediction based on the cloud platform has better efficiency and real-time performance.
|
|
Received: 19 May 2020
|
|
|
|
| Cite this article: |
|
Wang Hongfei,Zhou Xin,Xu Zhezhuang等. Analysis and forecast of industrial power consumption based on cloud platform[J]. Electrical Engineering, 2020, 21(12): 6-11.
|
|
|
|
| URL: |
|
http://dqjs.cesmedia.cn/EN/Y2020/V21/I12/6
|
[1] 李明, 胡殿刚, 周有学. 基于“两个替代”战略的甘肃新能源就地消纳模式研究与实践[J]. 电网技术, 2016, 40(10): 2991-2997.
[2] ZHANG Z Y, WANG X J, JI Y Y.The power load forecasting of SVR based on hadoop[C]//In 2018 37th Chinese Control Conference (CCC), 2018: 4484-4488.
[3] 颜金木. 基于趋势外推法的电力负荷预测[J]. 中国外资: 下半月, 2011(2): 228.
[4] BIANCO V, MANCA O, NARDINI S.Electricity con- sumption forecasting in Italy using linear regression models[J]. Energy, 2009, 34(9): 1413-1421.
[5] CORTES C, VAPNIK V.Support-vector networks[J]. Machine Learning, 1995, 20(3): 273-297.
[6] 姚佳馨, 田慧欣. 基于改进数据驱动子空间算法的电力负荷预测[J]. 计算机工程, 2015, 41(5): 311-315.
[7] CHENG Y F, JIN L J, HOU K. Short-term power load forecasting based on improved online ELM-K[C]//2018 International Conference on Control, Automation and Information Sciences (ICCAIS), 2018.
[8] 梅雅鑫. 阿里云:打造三层边缘计算能力构建云边端协同的开放生态[J]. 通信世界, 2019(11): 44.
[9] 洪国平, 李银娥, 孙新德, 等. 武汉市电网用电量、电力负荷与气温的关系及预测模型研究[J]. 华中电力, 2006, 19(2): 4-7, 30.
[10] 罗慧, 徐军昶, 肖波, 等. 气象因子对西安城市用电量的影响研究及中长期系统化预测[J]. 气象, 2016, 42(1): 54-60.
[11] LI Y C, FANG T J, YU E K.Study of support vector machines for short-term loadforecasting[J]. Proceedings- Chinese Society of Electrical Engineering, 2003, 23(6): 55-59.
[12] 周徐, 方东旭, 文冰松. 一种基于GBDT机器学习的算法及应用研究[J]. 电信工程技术与标准化, 2019, 32(11): 27-30, 46. |
|
|
|
2020, Vol. 21 
