Research On Heating Load Forecasting Based On Room Temperature Demand Analysis

Heat load forecasting is a technology that predicts the heating load required by buildings in the future through mathematical models and calculation methods,based on various factors such as historical load,indoor and outdoor temperature.Accurate forecasting of heat load can assist operators in regulating the heating system rationally,thereby improving system operating efficiency,reducing heating costs,and meeting user needs.With the increasing popularity of household metering systems in many countries,more and more heating users are installing heat meters and room temperature monitoring devices.As a result,heat supply units can obtain a large amount of room temperature and heat load data.By analyzing this data,heat supply units can better understand the user’s actual room temperature demand and the change in heat load,and predict the heat load for a period of time in the future,providing support for the precise regulation of the heating system.Therefore,the key to reducing heating energy consumption lies in two points: one is to accurately identify the room temperature demand of heating users,and the other is to establish a heat load prediction model based on user demand.In response to the aforementioned concerns,this study proposes an ARIMAXbased heat load prediction method that takes into account the room temperature requirements of users.This method involves two key steps.Firstly,the FCM clustering algorithm is used to analyze the room temperature data of heating users in the past 30 days,to identify the user’s room temperature demand range for the next day.Secondly,the ARIMAX model is utilized to analyze the indoor temperature,outdoor temperature,and heat load of heating users in the past 720 hours,which generates an ARIMAX heat load single-step prediction model.Finally,the room temperature demand is substituted into the model,and the heat load prediction value that meets the user’s demand at the next moment is obtained.In this study,a residential building located in Xigong District of Luoyang City was selected as the research subject.By applying the proposed heat load forecasting method to analyze the heating data of 119 heating users in the building during the period from December 2,2021 to January 14,2022,the heat load prediction value of the building users for the period from January 1 to January 14 was obtained.A comparison between the measured heat load and the predicted heat load of the building users was carried out,demonstrating that following the heat load prediction and heating system regulation based on user’s room temperature demand can achieve a cumulative energy saving rate of approximately 3.41% during the 14-day period of the study.The results confirm that the ARIMAX heat load prediction method proposed in this paper has a considerable energy-saving effect,and can provide valuable data support for future heating system regulation.