Abstract
Nowadays, industrial parks are seen as spaces for the integration of demand and electricity generation. The proximity of the industrial parks to the Smart City, makes possible the employment of advanced techniques for the prediction of the demand and electric generation. This paper presents a complete experiment to choose a model of Short-Term Load Forecasting in industrial parks. The models used are based on artificial intelligence, and different input variables have been tested on all models.
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References
Lazaroiu, G.C., Roscia, M.: Definition methodology for the smart cities model. Energy 47(1), 326–332 (2012)
Lund, H., Østergaard, P.A., Connolly, D., Mathiesen, B.V.: Smart energy and smart energy systems. Energy 137, 556–565 (2017)
Anderson, A., et al.: Empowering smart communities: Electrification, education, and sustainable entrepreneurship in IEEE smart village initiatives. IEEE Electrification Mag. 5(2), 6–16 (2017)
Valero, A., Usón, S., Torres, C., Valero, A., Agudelo, A., Costa, J.: Thermoeconomic tools for the analysis of eco-industrial parks. Energy 62, 62–72 (2013)
De Luca, G., Fabozzi, S., Massarotti, N., Vanoli, L.: A renewable energy system for a nearly zero greenhouse city: case study of a small city in southern Italy. Energy 143, 347–362 (2018)
Mirez, J., Hernandez-Callejo, L., Horn, M., Bonilla, L.-M.: Simulation of direct current microgrid and study of power and battery charge/discharge management. DYNA 92(6), 673–679 (2017)
Timmerman, J., Vandevelde, L., Van Eetvelde, G.: Towards low carbon business park energy systems: classification of techno-economic energy models. Energy 75, 68–80 (2014)
Jin, D.-G., Choi, J.-C., Won, D.-J., Lee, H.-J., Chae, W.-K., Park, J.-S.: A practical protection coordination strategy applied to secondary and facility microgrids. Energies 5(9), 3248–3265 (2012)
Gkatzikis, L., Koutsopoulos, I., Salonidis, T.: The role of aggregators in smart grid demand response markets. IEEE J. Sel. Areas Commun. 31(7), 1247–1257 (2013)
Hernandez, L., et al.: A survey on electric power demand forecasting: future trends in smart grids, microgrids and smart buildings. IEEE Commun. Surv. Tutor. 16(3), 1460–1495 (2014)
Hernández, L., et al.: Artificial neural network for short-term load forecasting in distribution systems. Energies 7(3), 1576–1598 (2014)
Amjady, N., Keynia, F., Zareipour, H.: Short-term load forecast of microgrids by a new bilevel prediction strategy. IEEE Trans. Smart Grid 1(3), 286–294 (2010)
Hernández, L., Baladrón, C., Aguiar, J.M., Carro, B., Sánchez-Esguevillas, A.: Classification and clustering of electricity demand patterns in industrial parks. Energies 5(12), 5215–5228 (2012)
Vu, D.H., Muttaqi, K.M., Agalgaonkar, A.P.: A variance inflation factor and backward elimination based robust regression model for forecasting monthly electricity demand using climatic variables. Appl. Energy 140, 385–394 (2015)
Hernández, L.: Experimental analysis of the input variables relevance’s to forecast next day’s aggregated electric demand using neural networks. Energies 6(6), 2927–2948 (2013)
Breiman, L., Friedman, J.H., Olshen, R.A., Stone, C.J.: Classification and Regression Trees. Wadsworth International Group, Belmont (1984)
Rumelhart, D.E., Hinton, G.E., Williams, R.J.: Learning representations by back-propagating errors. Nature 323(6088), 533 (1986)
Vapnik, V.: The Nature of Statistical Learning Theory. Springer, New York (2013)
Friedman, J.H.: Greedy function approximation: a gradient boosting machine. Ann. Stat. 29, 1189–1232 (2001)
Pedregosa, F., et al.: Scikit-learn: machine learning in Python. J. Mach. Learn. Res. 12, 2825–2830 (2011)
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Hernández-Callejo, L., García-Pedrero, A., Alonso Gómez, V. (2019). A New Model for Short-Term Load Forecasting in an Industrial Park. In: Nesmachnow, S., Hernández Callejo, L. (eds) Smart Cities. ICSC-CITIES 2018. Communications in Computer and Information Science, vol 978. Springer, Cham. https://doi.org/10.1007/978-3-030-12804-3_3
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DOI: https://doi.org/10.1007/978-3-030-12804-3_3
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