- -

Forecasting Building Electric Consumption Patterns through Statistical Methods

RiuNet: Repositorio Institucional de la Universidad Politécnica de Valencia

Compartir/Enviar a

Citas

Estadísticas

  • Estadisticas de Uso

Forecasting Building Electric Consumption Patterns through Statistical Methods

Mostrar el registro completo del ítem

Serrano-Guerrero, X.; Siavichay, L.; Clairand, J.; Escrivá-Escrivá, G. (2020). Forecasting Building Electric Consumption Patterns through Statistical Methods. Springer Nature. 164-175. https://doi.org/10.1007/978-3-030-32033-1_16

Por favor, use este identificador para citar o enlazar este ítem: http://hdl.handle.net/10251/179855

Ficheros en el ítem

Thumbnail
Nombre: Serrano-GuerreroS ...
Tamaño: 887.9Kb
Formato: PDF
Descripción: Versión del Autor.
Abrir/Preview
[Cerrado]
Nombre: Serrano-Guerrero_ ...
Tamaño: 2.103Mb
Formato: PDF
Descripción: Versión editorial
Solicitar una copia al autor

Metadatos del ítem

Título: Forecasting Building Electric Consumption Patterns through Statistical Methods
Autor: Serrano-Guerrero, Xavier Siavichay, Luis-Fernando Clairand, Jean-Michel Escrivá-Escrivá, Guillermo
Entidad UPV: Universitat Politècnica de València. Departamento de Ingeniería Eléctrica - Departament d'Enginyeria Elèctrica
Fecha difusión:
Resumen:
[EN] The electricity sector presents new challenges in the operation and planning of power systems, such as the forecast of power demand. This paper proposes a comprehensive approach for evaluating statistical methods and ...[+]
Palabras clave: ARIMA , Electric demand , Forecast , Load uncertainties , Statistical methods , Winter
Derechos de uso: Reserva de todos los derechos
ISBN: 978-3-030-32022-5
Fuente:
Advances in Emerging Trends and Technologies. (issn: 2194-5365 )
DOI: 10.1007/978-3-030-32033-1_16
Editorial:
Springer Nature
Versión del editor: https://doi.org/10.1007/978-3-030-32033-1_16
Título del congreso: 1er Congreso Internacional sobre Avances en Nuevas Tendencias y Tecnologías (ICAETT 2019)
Lugar del congreso: Quito, Ecuador
Fecha congreso: Mayo 29-31,2019
Serie: Advances in Intelligent Systems and Computing;1067
Código del Proyecto:
info:eu-repo/grantAgreement///6602277-01//Apoyo tecnológico entre la UPV y la Universidad Politécnica Salesiana para la mejora de la eficiencia energética en edificaciones de la zona ecuatorial de los Andes/
Tipo: Comunicación en congreso Artículo Capítulo de libro

References

Abedinia, O., Amjady, N., Zareipour, H.: A new feature selection technique for load and price forecast of electrical power systems. IEEE Trans. Power Syst. 32(1), 62–74 (2017). https://doi.org/10.1109/TPWRS.2016.2556620

Amber, K.P., Ahmad, R., Aslam, M.W., Kousar, A., Usman, M., Khan, M.S.: Intelligent techniques for forecasting electricity consumption of buildings. Energy 157, 886–893 (2018). https://doi.org/10.1016/j.energy.2018.05.155

Bianchi, F.M., Santis, E.D.E., Rizzi, A., Sadeghian, A.: Short-term electric load forecasting using echo state networks and PCA decomposition. IEEE Access 3, 1931–1943 (2015). https://doi.org/10.1109/ACCESS.2015.2485943 [+]
Abedinia, O., Amjady, N., Zareipour, H.: A new feature selection technique for load and price forecast of electrical power systems. IEEE Trans. Power Syst. 32(1), 62–74 (2017). https://doi.org/10.1109/TPWRS.2016.2556620

Amber, K.P., Ahmad, R., Aslam, M.W., Kousar, A., Usman, M., Khan, M.S.: Intelligent techniques for forecasting electricity consumption of buildings. Energy 157, 886–893 (2018). https://doi.org/10.1016/j.energy.2018.05.155

Bianchi, F.M., Santis, E.D.E., Rizzi, A., Sadeghian, A.: Short-term electric load forecasting using echo state networks and PCA decomposition. IEEE Access 3, 1931–1943 (2015). https://doi.org/10.1109/ACCESS.2015.2485943

Contreras, J., Espinola, R., Nogales, F.J., Conejo, A.J.: ARIMA models to predict next-day electricity prices. IEEE Trans. Power Syst. 22(9), 57–57 (2002). https://doi.org/10.1016/j.energy.2018.05.155 . http://ieeexplore.ieee.org/document/4312577/

Ding, N., Benoit, C., Foggia, G., Bésanger, Y., Wurtz, F.: Neural network-based model design for short-term load forecast in distribution systems. IEEE Trans. Power Syst. 31(1), 72–81 (2016). https://doi.org/10.1109/TPWRS.2015.2390132

Elias, C.N., Hatziargyriou, N.D.: An annual midterm energy forecasting model using fuzzy logic. IEEE Trans. Power Syst. 24(1), 469–478 (2009)

González-Romera, E., Jaramillo-Morán, M.Á., Carmona-Fernández, D.: Monthly electric energy demand forecasting based. IEEE Trans. Power Syst. 21(4), 1946–1953 (2006)

Hippert, H.S., Pedreira, C.E., Souza, R.C.: Neural networks for short-term load forecasting: a review and evaluation Full Text as PDF Full Text in HTML. IEEE Trans. Power Syst. 16(1), 4333 (2014)

Khosravi, A., Nahavandi, S.: Load forecasting using interval type-2 fuzzy logic systems: optimal type reduction. IEEE Trans. Ind. Inform. 10(2), 1055–1063 (2014). https://doi.org/10.1109/TII.2013.2285650

Kroposki, B., et al.: Achieving a 100% renewable grid: operating electric power systems with extremely high levels of variable renewable energy. IEEE Power Energy Mag. 15(2), 61–73 (2017). https://doi.org/10.1109/MPE.2016.2637122

Liu, B., Nowotarski, J., Hong, T., Weron, R.: Probabilistic load forecasting via quantile regression averaging on sister forecasts. IEEE Trans. Smart Grid 8(2), 730–737 (2017). https://doi.org/10.1109/TSG.2015.2437877

Meira, E., Oliveira, D., Luiz, F., Oliveira, C.: Forecasting mid-long term electric energy consumption through bagging ARIMA and exponential smoothing methods. Energy 144, 776–788 (2018). https://doi.org/10.1016/j.energy.2017.12.049

Park, S., Han, S.: Demand power forecasting with data mining method in smart grid. In: 2017 IEEE Innovative Smart Grid Technologies - Asia (2017). https://doi.org/10.1109/ISGT-Asia.2017.8378423

Quilumba, F.L., Lee, W.J., Huang, H., Wang, D.Y., Szabados, R.L.: Using smart meter data to improve the accuracy of intraday load forecasting considering customer behavior similarities. IEEE Trans. Smart Grid 6(2), 911–918 (2015). https://doi.org/10.1109/TSG.2014.2364233 . http://ieeexplore.ieee.org/document/6945384/

Raza, M.Q., Mithulananthan, N., Li, J., Lee, K.Y.: Multivariate ensemble forecast framework for demand prediction of anomalous days. IEEE Trans. Sustain. Energy 3029(c), 1–9 (2018). https://doi.org/10.1109/TSTE.2018.2883393

Serrano-Guerrero, X., Escrivá-Escrivá, G., Roldán-Blay, C.: Statistical methodology to assess changes in the electrical consumption profile of buildings. Energy Build. 164, 99–108 (2018). https://doi.org/10.1016/j.enbuild.2017.12.059

Serrano-Guerrero, X., Prieto-Galarza, R., Huilcatanda, E., Cabrera-Zeas, J., Escriva-Escriva, G.: Election of variables and short-term forecasting of electricity demand based on backpropagation artificial neural networks. In: 2017 IEEE International Autumn Meeting on Power, Electronics and Computing (ROPEC), ROPEC 2017, January 2018, pp. 1–5 (2018). https://doi.org/10.1109/ROPEC.2017.8261630

Tao, S., Li, Y., Xiao, X., Yao, L.: Load forecasting based on short-term correlation clustering. In: 2017 IEEE Innovative Smart Grid Technologies - Asia, pp. 1–7. IEEE (2017). https://doi.org/10.1109/ISGT-Asia.2017.8378416

Vartanian, C., Bauer, R., Casey, L., Loutan, C., Narang, D., Patel, V.: Ensuring system reliability: distributed energy resources and bulk power system considerations. IEEE Power Energy Mag. 16(6), 52–63 (2018). https://doi.org/10.1109/MPE.2018.2863059

Ye, X.Z., Ji, T.Y., Li, M.S., Wu, Q.H.: A morphological filter-based local prediction method with multi-variable inputs for short-term load forecast. In: 2017 IEEE Innovative Smart Grid Technologies - Asia (2017). https://doi.org/10.1109/ISGT-Asia.2017.8378323

[-]

recommendations

 

Este ítem aparece en la(s) siguiente(s) colección(ones)

Mostrar el registro completo del ítem