Estimating adaptive setpoint temperatures using weather stations
| Autor: | Juan Luis Pérez Ordóñez, Fernando Martínez Abella, Carlos Rubio Bellido, David Bienvenido-Huertas |
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| Přispěvatelé: | Universidad de Sevilla. Departamento de Construcciones Arquitectónicas II (ETSIE) |
| Rok vydání: | 2019 |
| Předmět: |
adaptive setpoint temperature
Control and Optimization 020209 energy Energy Engineering and Power Technology Context (language use) 02 engineering and technology 010501 environmental sciences 01 natural sciences lcsh:Technology Weather station Setpoint Control theory 0202 electrical engineering electronic engineering information engineering Multilayer perceptron multilayer perceptron Electrical and Electronic Engineering Engineering (miscellaneous) 0105 earth and related environmental sciences weather station Consumption (economics) Observational error Renewable Energy Sustainability and the Environment lcsh:T Adaptive setpoint temperature Energy consumption Multivariable linear regression Environmental science multivariable linear regression Predictive modelling Energy (miscellaneous) |
| Zdroj: | idUS. Depósito de Investigación de la Universidad de Sevilla instname Energies, Vol 12, Iss 7, p 1197 (2019) Energies Volume 12 Issue 7 RUC. Repositorio da Universidade da Coruña |
| Popis: | Reducing both the energy consumption and CO2 emissions of buildings is nowadays one of the main objectives of society. The use of heating and cooling equipment is among the main causes of energy consumption. Therefore, reducing their consumption guarantees such a goal. In this context, the use of adaptive setpoint temperatures allows such energy consumption to be significantly decreased. However, having reliable data from an external temperature probe is not always possible due to various factors. This research studies the estimation of such temperatures without using external temperature probes. For this purpose, a methodology which consists of collecting data from 10 weather stations of Galicia is carried out, and prediction models (multivariable linear regression (MLR) and multilayer perceptron (MLP)) are applied based on two approaches: (1) using both the setpoint temperature and the mean daily external temperature from the previous day and (2) using the mean daily external temperature from the previous 7 days. Both prediction models provide adequate performances for approach 1, obtaining accurate results between 1 month (MLR) and 5 months (MLP). However, for approach 2, only the MLP obtained accurate results from the 6th month. This research ensures the continuity of using adaptive setpoint temperatures even in case of possible measurement errors or failures of the external temperature probes. |
| Databáze: | OpenAIRE |
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