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Heat load forecasting using adaptive temporal hierarchies

Bergsteinsson, Hjörleifur G. (författare)
Technical University of Denmark
Møller, Jan Kloppenborg (författare)
Technical University of Denmark
Nystrup, Peter (författare)
Lund University,Lunds universitet,Matematisk statistik,Matematikcentrum,Institutioner vid LTH,Lunds Tekniska Högskola,Mathematical Statistics,Centre for Mathematical Sciences,Departments at LTH,Faculty of Engineering, LTH,Technical University of Denmark
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Pálsson, Ólafur Pétur (författare)
University of Iceland
Guericke, Daniela (författare)
Technical University of Denmark
Madsen, Henrik (författare)
Technical University of Denmark,Norwegian University of Science and Technology
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 (creator_code:org_t)
Elsevier BV, 2021
2021
Engelska.
Ingår i: Applied Energy. - : Elsevier BV. - 0306-2619. ; 292
  • Tidskriftsartikel (refereegranskat)
Abstract Ämnesord
Stäng  
  • Heat load forecasts are crucial for energy operators in order to optimize the energy production at district heating plants for the coming hours. Furthermore, forecasts of heat load are needed for optimized control of the district heating network since a lower temperature reduces the heat loss, but the required heat supply at the end-users puts a lower limit on the temperature level. Consequently, improving the accuracy of heat load forecasts leads to savings and reduced heat loss by enabling improved control of the network and an optimized production schedule at the plant. This paper proposes the use of temporal hierarchies to enhance the accuracy of heat load forecasts in district heating. Usually, forecasts are only made at the temporal aggregation level that is the most important for the system. However, forecasts for multiple aggregation levels can be reconciled and lead to more accurate forecasts at essentially all aggregation levels. Here it is important that the auto- and cross-covariance between forecast errors at the different aggregation levels are taken into account. This paper suggests a novel framework using temporal hierarchies and adaptive estimation to improve heat load forecast accuracy by optimally combining forecasts from multiple aggregation levels using a reconciliation process. The weights for the reconciliation are computed using an adaptively estimated covariance matrix with a full structure, enabling the process to share time-varying information both within and between aggregation levels. The case study shows that the proposed framework improves the heat load forecast accuracy by 15% compared to commercial state-of-the-art operational forecasts.

Ämnesord

TEKNIK OCH TEKNOLOGIER  -- Maskinteknik -- Energiteknik (hsv//swe)
ENGINEERING AND TECHNOLOGY  -- Mechanical Engineering -- Energy Engineering (hsv//eng)

Nyckelord

Adaptive estimator
Adaptive forecasting
Forecast reconciliation
Heat load forecast
Recursive shrinkage estimator
Temporal hierarchies

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