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Linear and neural network-based models for short-term heat load forecasting
Potočnik, Primož (Author), Strmčnik, Ervin (Author), Govekar, Edvard (Author)

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Abstract
Successful operation of a district heating system requires optimal scheduling of heating resources to satisfy heating demands. The optimal operation, therefore, requires accurate short-term forecasts of future heat load. In this paper, short-term forecasting of heat load in a district heating system of Ljubljana is presented. Heat load data and weather-related influential variables for five subsequent winter seasons of district heating operation are applied in this study. Various linear models and nonlinear neural network-based forecasting models are developed to forecast the future daily heat load with the forecasting horizon one day ahead. The models are evaluated based on generalization error, obtained on an independent test data set. Results demonstrate the importance of outdoor temperature as the most important influential variable. Other influential inputs include solar radiation and extracted features denoting population activities (such as day of the week). Comparison of forecasting models reveals good forecasting performance of a linear stepwise regression model (SR) that utilizes only a subset of the most relevant input variables. The operation of the SR model was improved by using neural network (NN) models, and also NN models with a direct linear link (NNLL). The latter showed the overall best forecasting performance, which suggests that NN or the proposed NNLL structures should be considered as forecasting solutions for applied forecasting in district heating markets.

Language:English
Keywords:district heating, heat load forecasting, feature extraction, stepwise regression, autoregressive model, neural networks
Work type:Article (dk_c)
Tipology:1.01 - Original Scientific Article
Organization:FS - Faculty of Mechanical Engineering
Year:2015
Number of pages:str. 543-550, SI 99
Numbering:Vol. 61, no. 9
UDC:681.5(045)
ISSN on article:0039-2480
URN:URN:NBN:SI:doc-6F110GT4
DOI:10.5545/sv-jme.2015.2548 Link is opened in a new window
COBISS.SI-ID:14183195 Link is opened in a new window
Views:112
Downloads:208
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Record is a part of a journal

Title:Strojniški vestnik
Shortened title:Stroj. vestn.
Publisher:Zveza strojnih inženirjev in tehnikov Slovenije [et al.], = Association of Mechanical Engineers and Technicians of Slovenia [et al.]
ISSN:0039-2480
COBISS.SI-ID:762116 New window

Document is financed by a project

Funder:ARRS - Agencija za raziskovalno dejavnost Republike Slovenije
Funding Programme:Raziskovalni projekti - aplikativni
Project no.:P2-0241
Name:Sinergetika kompleksnih sistemov in procesov
Acronym:
Project ID:info:eu-repo/grantAgreement/ARRS/Raziskovalni%20projekti%20-%20aplikativni/P2-0241

Secondary language

Language:Slovenian
Abstract:
Raziskava obravnava problematiko kratkoročnega napovedovanja odjema toplote v sistemu daljinskega ogrevanja. Kakovostne napovedi odjema toplote v vročevodnem sistemu so zelo pomembne s stališča učinkovite rabe energije, ki zahteva usklajevanje prihodnih potreb odjemalcev ter proizvodnje in dobave ustreznih količin toplote. Napovedovanje odjema toplote sodi zaradi prisotnosti kompleksnih procesov med zahtevnejše naloge daljinskega ogrevanja, kratkoročne napovedi pa so direktno uporabne za učinkovito krmiljenje in optimizacijo sistema daljinskega ogrevanja.

Keywords:daljinsko ogrevanje, napovedovanje odjema toplote, izpeljava značilk, stopenjska regresija, avtoregresijski model, nevronske mreže

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