Evaluation of a multiple linear regression model and SARIMA model in forecasting heat demand for district heating system

Forecasting heat demand is necessary for production and operation planning of district heating (DH) systems. In this study we first propose a simple regression model where the hourly outdoor temperature and wind speed forecast the heat demand. Weekly rhythm of heat consumption as a social component is added to the model to significantly improve the accuracy. The other type of model is the seasonal autoregressive integrated moving average (SARIMA) model with exogenous variables as a combination to take weather factors, and the historical heat consumption data as depending variables. One outstanding advantage of the model is that it peruses the high accuracy for both long-term and short-term forecast by considering both exogenous factors and time series. The forecasting performance of both linear regression models and time series model are evaluated based on real-life heat demand data for the city of Espoo in Finland by out-of-sample tests for the last 20 full weeks of the year. The results indicate that the proposed linear regression model (T168h) using 168-h demand pattern with midweek holidays classified as Saturdays or Sundays gives the highest accuracy and strong robustness among all the tested models based on the tested forecasting horizon and corresponding data. Considering the parsimony of the input, the ease of use and the high accuracy, the proposed T168h model is the best in practice. The heat demand forecasting model can also be developed for individual buildings if automated meter reading customer measurements are available. This would allow forecasting the heat demand based on more accurate heat consumption data directly from the customers and almost in real time. Also, the model can be used for production planning of combined heat and power (CHP) system to improve the energy efficiency.

[1]  Shahaboddin Shamshirband,et al.  Intelligent forecasting of residential heating demand for the District Heating System based on the monthly overall natural gas consumption , 2015 .

[2]  Seyed Farid Ghaderi,et al.  Energy demand forecasting in Iranian metal industry using linear and nonlinear models based on evolutionary algorithms , 2012 .

[3]  V. Ediger,et al.  ARIMA forecasting of primary energy demand by fuel in Turkey , 2007 .

[4]  M. Smith,et al.  A comparison of periodic autoregressive and dynamic factor models in intraday energy demand forecasting , 2013 .

[5]  Luca A. Tagliafico,et al.  Heating and cooling building energy demand evaluation; a simplified model and a modified degree days approach , 2014 .

[6]  Cheol-Yong Jang,et al.  Feasibility study on a novel methodology for short-term real-time energy demand prediction using weather forecasting data , 2013 .

[7]  Sven Werner.  District Heating and Cooling , 2013 .

[8]  Muditha Abeysekera,et al.  Modelling of Energy Demand in a Modern Domestic Dwelling , 2015 .

[9]  Risto Lahdelma,et al.  Forecasting District Heating Consumption based on Customer Measurements , 2012 .

[10]  Risto Lahdelma,et al.  Genetic optimization of multi-plant heat production in district heating networks , 2015 .

[11]  Risto Lahdelma,et al.  Optimization of combined heat and power production with heat storage based on sliding time window method , 2016 .

[12]  Tiberiu Catalina,et al.  Multiple regression model for fast prediction of the heating energy demand , 2013 .

[13]  Christian Inard,et al.  Fast method to predict building heating demand based on the design of experiments , 2009 .

[14]  Wil L. Kling,et al.  Pseudo Dynamic Transitional Modeling of Building Heating Energy Demand Using Artificial Neural Network , 2014, ArXiv.

[15]  Paul S. Fischbeck,et al.  Predicting residential energy and water demand using publicly available data , 2015 .

[16]  Taehoon Hong,et al.  A Lagrangian finite element model for estimating the heating and cooling demand of a residential building with a different envelope design , 2015 .

[17]  Marc A. Rosen,et al.  District heating and cooling: Review of technology and potential enhancements , 2012 .

[18]  Targo Kalamees,et al.  Energy demand for the heating and cooling of residential houses in Finland in a changing climate , 2015 .

[19]  Qie Sun,et al.  Statistical analysis of energy consumption patterns on the heat demand of buildings in district heating systems , 2014 .

[20]  Kristina Holmgren,et al.  Role of a district-heating network as a user of waste-heat supply from various sources : the case of Göteborg , 2006 .

[21]  R. E. Abdel-Aal,et al.  Univariate modeling and forecasting of monthly energy demand time series using abductive and neural networks , 2008, Comput. Ind. Eng..

[22]  K. Wojdyga,et al.  An influence of weather conditions on heat demand in district heating systems , 2008 .

[23]  Erik Dotzauer,et al.  Simple model for prediction of loads in district-heating systems , 2002 .

[24]  A. Harvey.  Time series models , 1983 .

[25]  Shahaboddin Shamshirband,et al.  Forecasting of consumers heat load in district heating systems using the support vector machine with a discrete wavelet transform algorithm , 2015 .

[26]  Marwa M. Hassan,et al.  Development and validation of a simple estimating tool to predict heating and cooling energy demand for attics of residential buildings , 2012 .

[27]  Kody M. Powell,et al.  Heating, cooling, and electrical load forecasting for a large-scale district energy system , 2014 .