Data driven model for heat load prediction in buildings connected to District Heating by using smart heat meters
| Autor: | Koldobika Martin-Escudero, Mikel Lumbreras, Margus Raud, Roberto Garay-Martinez, Indrek Hagu, Gonzalo Diarce, Beñat Arregi |
|---|---|
| Přispěvatelé: | European Commission |
| Jazyk: | angličtina |
| Rok vydání: | 2021 |
| Předmět: |
data-driven model
020209 energy load forecasting 02 engineering and technology heat meters 7. Clean energy Industrial and Manufacturing Engineering Wind speed Data-driven 020401 chemical engineering building 0202 electrical engineering electronic engineering information engineering Building Point (geometry) District Heating 0204 chemical engineering Electrical and Electronic Engineering district heating Civil and Structural Engineering Load forecasting Mechanical Engineering Building and Construction Data-driven model Pollution Variable (computer science) General Energy 13. Climate action Software deployment Heat meters Environmental science Heat load Energy (signal processing) Predictive modelling Marine engineering |
| Zdroj: | Addi. Archivo Digital para la Docencia y la Investigación instname TECNALIA Publications Fundación Tecnalia Research & Innovation |
| Popis: | [EN] An accurate characterization and prediction of heat loads in buildings connected to a District Heating (DH) network is crucial for the effective operation of these systems. The high variability of the heat production process of DH networks with low supply temperatures and derived from the incorporation of different heat sources increases the need for heat demand prediction models. This paper presents a novel data-driven model for the characterization and prediction of heating demand in buildings connected to a DH network. This model is built on the so-called Q-algorithm and fed with real data from 42 smart energy meters located in 42 buildings connected to the DH in Tartu (Estonia). These meters deliver heat consumption data with a 1-h frequency. Heat load profiles are analysed, and a model based on supervised clustering methods in combination with multiple variable regression is proposed. The model makes use of four climatic variables, including outdoor ambient temperature, global solar radiation and wind speed and direction, combined with time factors and data from smart meters. The model is designed for deployment over large sets of the building stock, and thus aims to forecast heat load regardless of the construction characteristics or final use of the building. The low computational cost required by this algorithm enables its integration into machines with no special requirements due to the equations governing the model. The data-driven model is evaluated both statistically and from an engineering or energetic point of view. R-2 values from 0.70 to 0.99 are obtained for daily data resolution and R-2 values up to 0.95 for hourly data resolution. Hourly results are very promising for more than 90% of the buildings under study. ( This study has been carried out in the context of RELaTED project. This project has received funding from the European Union's Horizon 2020 research and innovation programme under grant agreement No 768567. This publication reflects only the authors' views and neither the Agency nor the Commission are responsible for any use that may be made of the information contained therein. |
| Databáze: | OpenAIRE |
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