IDEAS home Printed from https://ideas.repec.org/a/gam/jeners/v12y2019i7p1309-d220279.html
   My bibliography  Save this article

Methodology for the Quantification of the Impact of Weather Forecasts in Predictive Simulation Models

Author

Listed:
  • Eva Lucas Segarra

    (School of Architecture, University of Navarra, 31009 Pamplona, Spain)

  • Hu Du

    (Welsh School of Architecture, Cardiff University, Bute Building, King Edward VII Avenue, Cardiff, Wales CF10 3NB, UK)

  • Germán Ramos Ruiz

    (School of Architecture, University of Navarra, 31009 Pamplona, Spain)

  • Carlos Fernández Bandera

    (School of Architecture, University of Navarra, 31009 Pamplona, Spain)

Abstract

The use of Building Energy Models (BEM) has become widespread to reduce building energy consumption. Projection of the model in the future to know how different consumption strategies can be evaluated is one of the main applications of BEM. Many energy management optimization strategies can be used and, among others, model predictive control (MPC) has become very popular nowadays. When using models for predicting the future, we have to assume certain errors that come from uncertainty parameters. One of these uncertainties is the weather forecast needed to predict the building behavior in the near future. This paper proposes a methodology for quantifying the impact of the error generated by the weather forecast in the building’s indoor climate conditions and energy demand. The objective is to estimate the error introduced by the weather forecast in the load forecasting to have more precise predicted data. The methodology employed site-specific, near-future forecast weather data obtained through online open access Application Programming Interfaces (APIs). The weather forecast providers supply forecasts up to 10 days ahead of key weather parameters such as outdoor temperature, relative humidity, wind speed and wind direction. This approach uses calibrated EnergyPlus models to foresee the errors in the indoor thermal behavior and energy demand caused by the increasing day-ahead weather forecasts. A case study investigated the impact of using up to 7-day weather forecasts on mean indoor temperature and energy demand predictions in a building located in Pamplona, Spain. The main novel concepts in this paper are: first, the characterization of the weather forecast error for a specific weather data provider and location and its effect in the building’s load prediction. The error is calculated based on recorded hourly data so the results are provided on an hourly basis, avoiding the cancel out effect when a wider period of time is analyzed. The second is the classification and analysis of the data hour-by-hour to provide an estimate error for each hour of the day generating a map of hourly errors. This application becomes necessary when the building takes part in the day-ahead programs such as demand response or flexibility strategies, where the predicted hourly load must be provided to the grid in advance. The methodology developed in this paper can be extrapolated to any weather forecast provider, location or building.

Suggested Citation

  • Eva Lucas Segarra & Hu Du & Germán Ramos Ruiz & Carlos Fernández Bandera, 2019. "Methodology for the Quantification of the Impact of Weather Forecasts in Predictive Simulation Models," Energies, MDPI, vol. 12(7), pages 1-16, April.
    • Handle: RePEc:gam:jeners:v:12:y:2019:i:7:p:1309-:d:220279
    as

    Download full text from publisher

    File URL: https://www.mdpi.com/1996-1073/12/7/1309/pdf
    Download Restriction: no
    File URL: https://www.mdpi.com/1996-1073/12/7/1309/
    Download Restriction: no
    ---><---

    References listed on IDEAS

    as
    1. Mirakyan, Atom & Meyer-Renschhausen, Martin & Koch, Andreas, 2017. "Composite forecasting approach, application for next-day electricity price forecasting," Energy Economics, Elsevier, vol. 66(C), pages 228-237.
    2. Lazos, Dimitris & Sproul, Alistair B. & Kay, Merlinde, 2014. "Optimisation of energy management in commercial buildings with weather forecasting inputs: A review," Renewable and Sustainable Energy Reviews, Elsevier, vol. 39(C), pages 587-603.
    3. Tobias Heidrich & Jonathan Grobe & Henning Meschede & Jens Hesselbach, 2018. "Economic Multiple Model Predictive Control for HVAC Systems—A Case Study for a Food Manufacturer in Germany," Energies, MDPI, vol. 11(12), pages 1-18, December.
    4. Petojević, Zorana & Gospavić, Radovan & Todorović, Goran, 2018. "Estimation of thermal impulse response of a multi-layer building wall through in-situ experimental measurements in a dynamic regime with applications," Applied Energy, Elsevier, vol. 228(C), pages 468-486.
    5. Agüera-Pérez, Agustín & Palomares-Salas, José Carlos & González de la Rosa, Juan José & Florencias-Oliveros, Olivia, 2018. "Weather forecasts for microgrid energy management: Review, discussion and recommendations," Applied Energy, Elsevier, vol. 228(C), pages 265-278.
    6. Amin Mohammadi & Mahmoud Reza Saghafi & Mansoureh Tahbaz & Farshad Nasrollahi, 2017. "Effects of Vernacular Climatic Strategies (VCS) on Energy Consumption in Common Residential Buildings in Southern Iran: The Case Study of Bushehr City," Sustainability, MDPI, vol. 9(11), pages 1-26, October.
    7. Jing Zhao & Yaoqi Duan & Xiaojuan Liu, 2018. "Uncertainty Analysis of Weather Forecast Data for Cooling Load Forecasting Based on the Monte Carlo Method," Energies, MDPI, vol. 11(7), pages 1-18, July.
    8. Ramos Ruiz, Germán & Fernández Bandera, Carlos, 2017. "Analysis of uncertainty indices used for building envelope calibration," Applied Energy, Elsevier, vol. 185(P1), pages 82-94.
    9. Germán Ramos Ruiz & Carlos Fernández Bandera, 2017. "Validation of Calibrated Energy Models: Common Errors," Energies, MDPI, vol. 10(10), pages 1-19, October.
    10. Baris Yuce & Monjur Mourshed & Yacine Rezgui, 2017. "A Smart Forecasting Approach to District Energy Management," Energies, MDPI, vol. 10(8), pages 1-22, July.
    11. Petersen, Steffen & Svendsen, Svend, 2011. "Method for simulating predictive control of building systems operation in the early stages of building design," Applied Energy, Elsevier, vol. 88(12), pages 4597-4606.
    12. Ramos Ruiz, Germán & Fernández Bandera, Carlos & Gómez-Acebo Temes, Tomás & Sánchez-Ostiz Gutierrez, Ana, 2016. "Genetic algorithm for building envelope calibration," Applied Energy, Elsevier, vol. 168(C), pages 691-705.
    13. Ljubomir Jankovic, 2018. "Designing Resilience of the Built Environment to Extreme Weather Events," Sustainability, MDPI, vol. 10(1), pages 1-14, January.
    Full references (including those not matched with items on IDEAS)

    Citations

    Citations are extracted by the CitEc Project, subscribe to its RSS feed for this item.
    as


    Cited by:

    1. Thilker, Christian Ankerstjerne & Madsen, Henrik & Jørgensen, John Bagterp, 2021. "Advanced forecasting and disturbance modelling for model predictive control of smart energy systems," Applied Energy, Elsevier, vol. 292(C).
    2. Eva Lucas Segarra & Germán Ramos Ruiz & Vicente Gutiérrez González & Antonis Peppas & Carlos Fernández Bandera, 2020. "Impact Assessment for Building Energy Models Using Observed vs. Third-Party Weather Data Sets," Sustainability, MDPI, vol. 12(17), pages 1-27, August.
    3. Andrés Jonathan Guízar Dena & Miguel Ángel Pascual & Carlos Fernández Bandera, 2021. "Building Energy Model for Mexican Energy Standard Verification Using Physics-Based Open Studio SGSAVE Software Simulation," Sustainability, MDPI, vol. 13(3), pages 1-34, February.
    4. Vicente Gutiérrez González & Germán Ramos Ruiz & Carlos Fernández Bandera, 2021. "Impact of Actual Weather Datasets for Calibrating White-Box Building Energy Models Base on Monitored Data," Energies, MDPI, vol. 14(4), pages 1-16, February.
    5. Joanna Kajewska-Szkudlarek & Jan Bylicki & Justyna Stańczyk & Paweł Licznar, 2021. "Neural Approach in Short-Term Outdoor Temperature Prediction for Application in HVAC Systems," Energies, MDPI, vol. 14(22), pages 1-15, November.
    6. Luo, Na & Langevin, Jared & Chandra-Putra, Handi & Lee, Sang Hoon, 2022. "Quantifying the effect of multiple load flexibility strategies on commercial building electricity demand and services via surrogate modeling," Applied Energy, Elsevier, vol. 309(C).

    Most related items

    These are the items that most often cite the same works as this one and are cited by the same works as this one.
    1. Eva Lucas Segarra & Germán Ramos Ruiz & Vicente Gutiérrez González & Antonis Peppas & Carlos Fernández Bandera, 2020. "Impact Assessment for Building Energy Models Using Observed vs. Third-Party Weather Data Sets," Sustainability, MDPI, vol. 12(17), pages 1-27, August.
    2. Vicente Gutiérrez González & Germán Ramos Ruiz & Carlos Fernández Bandera, 2021. "Impact of Actual Weather Datasets for Calibrating White-Box Building Energy Models Base on Monitored Data," Energies, MDPI, vol. 14(4), pages 1-16, February.
    3. Carlos Fernández Bandera & Germán Ramos Ruiz, 2017. "Towards a New Generation of Building Envelope Calibration," Energies, MDPI, vol. 10(12), pages 1-19, December.
    4. Carlos Fernández Bandera & Jose Pachano & Jaume Salom & Antonis Peppas & Germán Ramos Ruiz, 2020. "Photovoltaic Plant Optimization to Leverage Electric Self Consumption by Harnessing Building Thermal Mass," Sustainability, MDPI, vol. 12(2), pages 1-20, January.
    5. Germán Ramos Ruiz & Eva Lucas Segarra & Carlos Fernández Bandera, 2018. "Model Predictive Control Optimization via Genetic Algorithm Using a Detailed Building Energy Model," Energies, MDPI, vol. 12(1), pages 1-18, December.
    6. Thomas Schmitt & Tobias Rodemann & Jürgen Adamy, 2021. "The Cost of Photovoltaic Forecasting Errors in Microgrid Control with Peak Pricing," Energies, MDPI, vol. 14(9), pages 1-13, April.
    7. Chen, Hanfei & Lin, ChihChieh & Longtin, Jon P., 2019. "Dynamic modeling and parameter optimization of a free-piston Vuilleumier heat pump with dwell-based motion," Applied Energy, Elsevier, vol. 242(C), pages 741-751.
    8. Azuatalam, Donald & Paridari, Kaveh & Ma, Yiju & Förstl, Markus & Chapman, Archie C. & Verbič, Gregor, 2019. "Energy management of small-scale PV-battery systems: A systematic review considering practical implementation, computational requirements, quality of input data and battery degradation," Renewable and Sustainable Energy Reviews, Elsevier, vol. 112(C), pages 555-570.
    9. Al-Awsh, Waleed A. & Qasem, Naef A.A. & Al-Amoudi, Omar S. Baghabra & Al-Osta, Mohammed A., 2020. "Experimental and numerical investigation on innovative masonry walls for industrial and residential buildings," Applied Energy, Elsevier, vol. 276(C).
    10. Germán Ramos Ruiz & Carlos Fernández Bandera, 2017. "Validation of Calibrated Energy Models: Common Errors," Energies, MDPI, vol. 10(10), pages 1-19, October.
    11. Juricic, Sarah & Goffart, Jeanne & Rouchier, Simon & Foucquier, Aurélie & Cellier, Nicolas & Fraisse, Gilles, 2021. "Influence of natural weather variability on the thermal characterisation of a building envelope," Applied Energy, Elsevier, vol. 288(C).
    12. Carlos Fernández Bandera & Ana Fei Muñoz Mardones & Hu Du & Juan Echevarría Trueba & Germán Ramos Ruiz, 2018. "Exergy As a Measure of Sustainable Retrofitting of Buildings," Energies, MDPI, vol. 11(11), pages 1-19, November.
    13. Vicente Gutiérrez González & Lissette Álvarez Colmenares & Jesús Fernando López Fidalgo & Germán Ramos Ruiz & Carlos Fernández Bandera, 2019. "Uncertainy’s Indices Assessment for Calibrated Energy Models," Energies, MDPI, vol. 12(11), pages 1-18, May.
    14. Agüera-Pérez, Agustín & Palomares-Salas, José Carlos & González de la Rosa, Juan José & Florencias-Oliveros, Olivia, 2018. "Weather forecasts for microgrid energy management: Review, discussion and recommendations," Applied Energy, Elsevier, vol. 228(C), pages 265-278.
    15. Guo, Bowei & Newbery, David, 2021. "The cost of uncoupling GB interconnectors," Energy Policy, Elsevier, vol. 158(C).
    16. Žižak, Tej & Domjan, Suzana & Medved, Sašo & Arkar, Ciril, 2022. "Efficiency and sustainability assessment of evaporative cooling of photovoltaics," Energy, Elsevier, vol. 254(PA).
    17. Martín Pensado-Mariño & Lara Febrero-Garrido & Pablo Eguía-Oller & Enrique Granada-Álvarez, 2021. "Feasibility of Different Weather Data Sources Applied to Building Indoor Temperature Estimation Using LSTM Neural Networks," Sustainability, MDPI, vol. 13(24), pages 1-15, December.
    18. Abdelhakim Mesloub & Aritra Ghosh & Mabrouk Touahmia & Ghazy Abdullah Albaqawy & Emad Noaime & Badr M. Alsolami, 2020. "Performance Analysis of Photovoltaic Integrated Shading Devices (PVSDs) and Semi-Transparent Photovoltaic (STPV) Devices Retrofitted to a Prototype Office Building in a Hot Desert Climate," Sustainability, MDPI, vol. 12(23), pages 1-17, December.
    19. Kittisak Lohwanitchai & Daranee Jareemit, 2021. "Modeling Energy Efficiency Performance and Cost-Benefit Analysis Achieving Net-Zero Energy Building Design: Case Studies of Three Representative Offices in Thailand," Sustainability, MDPI, vol. 13(9), pages 1-24, May.
    20. Romero-Quete, David & Garcia, Javier Rosero, 2019. "An affine arithmetic-model predictive control approach for optimal economic dispatch of combined heat and power microgrids," Applied Energy, Elsevier, vol. 242(C), pages 1436-1447.

    Corrections

    All material on this site has been provided by the respective publishers and authors. You can help correct errors and omissions. When requesting a correction, please mention this item's handle: RePEc:gam:jeners:v:12:y:2019:i:7:p:1309-:d:220279. See general information about how to correct material in RePEc.

    If you have authored this item and are not yet registered with RePEc, we encourage you to do it here. This allows to link your profile to this item. It also allows you to accept potential citations to this item that we are uncertain about.

    If CitEc recognized a bibliographic reference but did not link an item in RePEc to it, you can help with this form .

    If you know of missing items citing this one, you can help us creating those links by adding the relevant references in the same way as above, for each refering item. If you are a registered author of this item, you may also want to check the "citations" tab in your RePEc Author Service profile, as there may be some citations waiting for confirmation.

    For technical questions regarding this item, or to correct its authors, title, abstract, bibliographic or download information, contact: MDPI Indexing Manager (email available below). General contact details of provider: https://www.mdpi.com .

    Please note that corrections may take a couple of weeks to filter through the various RePEc services.

    IDEAS is a RePEc service. RePEc uses bibliographic data supplied by the respective publishers.