基于大数据模式识别机器学习算法的热力站动态能耗指标预测模型

为了实现对供热系统热力站的热负荷预测，将天气、用户室内温度和时间迟滞性等因素作为负荷预测的数据依据。本文利用统计学原理和大数据架构，通过大量数据样本的学习和修正，为解决热力行业热力控制理论缺失问题提供了一条崭新的方法。将模式识别算法和时间序列相关性分析作为算法的核心，为解决天气和时间迟滞性对用户供热的影响提供了可能。本文以我公司n个典型热力站和其所带热用户为实验对象，以所在地区天气预报和天气实时数据为依据，对供暖期间所采集的热力站供暖数据、天气预报数据、典型供暖用户室内温度,通过大数据模式识别机器学习算法对样本进行学习训练，形成一套完整热力站动态能耗指标预测模型。

Dynamic Energy Consumption Index Forecast Model for Thermal Station Based on the Machine Learning Algorithm for Large Data Pattern Recognition

In order to predict the heat load of heating system, the weather, user''s indoor temperature and time lag are taken as the data basis. In this paper, a new method is provided to solve the problem of the lack of thermal control theory in thermal power industry by using statistical theory and big data structure and by studying and revising a lot of data samples. The core of the Algorithm is pattern recognition algorithm and correlation analysis of time series, which makes it possible to solve the influence of weather and time delay on user heating. In this paper, we take n typical thermal power stations and their tropical users as the experimental objects, and take the local weather forecast and real-time weather data as the basis, the samples were trained by big data pattern recognition machine learning algorithm based on heating station heating data, weather forecast data and typical heating user''s indoor temperature, a set of forecasting model for Dynamic Energy Consumption Index of thermal power station is formed.