کد مقاله | کد نشریه | سال انتشار | مقاله انگلیسی | نسخه تمام متن |
---|---|---|---|---|
6682191 | 501846 | 2016 | 9 صفحه PDF | دانلود رایگان |
عنوان انگلیسی مقاله ISI
Evaluation of a multiple linear regression model and SARIMA model in forecasting heat demand for district heating system
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کلمات کلیدی
CHPLSQSeasonal autoregressive integrated moving averageMAPETICPACFACFAICBICARMAXRMSEpartial autocorrelation function - تابع وابستگی خودکار جزئیCombined Heat and Power - ترکیب گرما و قدرتMaximum likelihood - حداکثر احتمالLinear regression - رگرسیون خطیroot mean squared error - ریشه متوسط خطای مربعSARIMA - سرماAutocorrelation function - عملکرد ارتباط خودکارBayesian information criterion - معیار اطلاعات بیزیAkaike information criterion - معیار اطلاعاتی آکائیکmean absolute percentage error - میانگین درصد خطای مطلقLeast squares - کمترین مربعاتGARCH - گارچDistrict heating - گرمایش ناحیه ای
موضوعات مرتبط
مهندسی و علوم پایه
مهندسی انرژی
مهندسی انرژی و فناوری های برق
پیش نمایش صفحه اول مقاله
چکیده انگلیسی
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.
ناشر
Database: Elsevier - ScienceDirect (ساینس دایرکت)
Journal: Applied Energy - Volume 179, 1 October 2016, Pages 544-552
Journal: Applied Energy - Volume 179, 1 October 2016, Pages 544-552
نویسندگان
Tingting Fang, Risto Lahdelma,