This paper presents a methodology for a high-resolution urban spatial load demand forecasting. This methodology is meant to improve the visualization, analysis and inference of load density information in the electric distribution systems in the near future. The proposed methodology converts input data into grid maps and then divides the grid map into larger regions, which will have their expected growth according to convolution matrices and weighting factors that search for characteristics in the history of this region. The definition of the characteristics of the region’s growth is obtained by processing the imperialist competitive algorithm that searches the best array of convolution, which will set the expected growth of the region. Thus, it is possible to obtain a spatial growth forecast of high resolution and with great precision, which are important factors for smart-grid planning.
K.R.M.Supapo, R.V.M.Santiago and M.C.Pacis, Electric load demand forecasting for Aborlan-Narra-Quezon distribution grid in Palawan using multiple linear regression, in 2017 IEEE 9th International Conference on Humanoid, Nanotechnology, Information Technology, Communication and Control, Environment and Management (HNICEM), 2017, pp. 1–6.
2.
Z.K.Pecenak, V.R.Disfani, M.J.Reno and J.Kleissl, Multiphase Distribution Feeder Reduction, IEEE Transactions on Power Systems33(2) (2018), 1320–1328.
3.
W.Zhang, G.Yan and G.Mu, A noval space-time feature extraction approach for load forecasting, in 2017 13th International Conference on Natural Computation, Fuzzy Systems and Knowledge Discovery (ICNC-FSKD), 2017, pp. 1953–1957.
4.
M.Grabner, Z.Bregar, S.Ivanjko and L.Valencic, Improved model for the spatial load forecasting of the Slovenian distribution network, CIRED - Open Access Proceedings Journal2017(1) (2017), 2354–2357.
5.
H.L.Willis, Spatial electric load demand forecasting, CRC Press, 2002.
6.
J.D.Melo, E.M.Carreno and A.Padilha-Feltrin, Multiagent simulation of urban social dynamics for spatial load demand forecasting, IEEE Transactions on Power Systems27(4) (2012), 1870–1878.
7.
P.A.Longley and M.Batty, Spatial analysis: Modelling in a GIS environment, John Wiley & Sons, 1996.
8.
H.G.Arango, G.Lambert-Torres, C.H.V.de Moraes and L.E.Borges da Silva, Spatiotemporal Data Mining for Distribution Load Expansion, Advances in Electrical and Computer Engineering16(3) (2016), 65–72.
9.
Y.He, J.Jiao, Q.Chen, S.Ge, Y.Chang and Y.Xu, Urban long term electricity demand forecast method based on system dynamics of the new economic normal: The case of Tianjin, Energy133 (2017), 9–22.
10.
E.M.Carreno, R.M.Rocha and A.Padilha-Feltrin, A cellular automaton approach to spatial electric load demand forecasting, IEEE Transactions on Power Systems26(2) (2011), 532–540.
11.
H.G.Arango, A.C.Z.Souza, G.Lambert-Torres and A.P.Alves da Silva, Difference between regular and deterministic chaos processes based on time analysis of load: An example using CEMIG data, Electric Power Systems Research56(1) (2000), 35–41.
12.
G.Lambert-Torres, J.M.Abe, M.L.Mucheroni and P.E.Cru-vinel, Advances in Intelligent Systems and Robotics, IOS Press, 2003.
13.
E.Atashpaz-Gargari and C.Lucas, Imperialist competitive algorithm: An algorithm for optimization inspired by imperialistic competition, in 2007 IEEE Congress on Evolutionary Computation, 2007, pp. 4661–4667. 10.1109/CEC.2007.4425083
14.
R.Roche, L.Idoumghar, B.Blunier and A.Miraoui, Imperialist competitive algorithm for dynamic optimization of economic dispatch in power systems, in International Conference on Artificial Evolution, Springer, 2011, pp. 217–228.
15.
C.I.de Almeida, G.Lambert-Torres, M.C.Barros Neto, G.C.Cunha de Andrade and C.H.Valerio de Moraes, An application of PHAP in power system planning, in 2008 IEEE/PES Transmission and Distribution Conference and Exposition: Latin America, 2008.
16.
I.Moghram and S.Rahman, Analysis and evaluation of five short-term load demand forecasting techniques, IEEE Transactions on Power Systems4(4) (1989), 1484–1491.
17.
H.Yu4and Q.Zhang, Application of variable structure artificial neural network for mid-long term load demand forecasting, in 2010 2nd IEEE International Conference on Information Management and Engineering, 2010, pp. 450–453.
18.
M.-Y.Chow and H.Tram, Methodology of urban re-development considerations in spatial load demand forecasting, IEEE Transactions on Power Systems12(2) (1997), 996–1001.
19.
M.-Y.Chow and H.Tram, Application of fuzzy logic technology for spatial load demand forecasting, IEEE Transactions on Power Systems12(3) (1997), 1360–1366.
20.
J.Wasilewski, Spatial Electric Load demand forecasting Using Fuzzy Cellular Automata Technique, Universities' Proceedings of the 46th International Power Engineering Conference (UPEC), Germany, 2011.
21.
J.Melo, A.Padilha-Feltrin and E.Carreno, Spatial pattern recognition of urban sprawl using a geographically weighted regression for spatial electric load demand forecasting, in 18th International Conference on Intelligent System Application to Power Systems (ISAP 2015), 2015, pp. 1–5.
22.
A.Dominguez-Torres, Origin and history of convolution, Special Report, CONACYT ActNumber BA90074, 2010, p. 41.
23.
A.A.Esmin, G.Lambert-Torres and A.C.Z.de Souza, A hybrid particle swarm optimization applied to loss power minimization, IEEE Transactions on Power Systems20(2) (2005), 859–866.
24.
N.L.Johnson, S.Kotz and N.Balakrishnan, Continuous multivariate distributions, volume 1, models and applications. New York:John Wiley & Sons, Vol. 59, 2002.
25.
E.W.Weisstein, Triangular distribution, MathWorld-A Wolfram Web Resource, 2001.
26.
M.Nakamura, Obtaining a normal sequence with prescribed covariances from an independent uniform distribution of random variables, IEEE Transactions on Systems Science and Cybernetics4(2) (1968), 191–191.
27.
A.A.A.Esmin and G.Lambert-Torres, Fitting Fuzzy Membership Functions using Hybrid Particle Swarm Optimization, in 2006 IEEE World Congress on Computational Intelligence & IEEE International Conference on Fuzzy Systems, 2006, pp. 9954–9961.
28.
B.A.Whitehead and T.D.Choate, Cooperative-competitive genetic evolution of radial basis function centers and widths for time series prediction, IEEE Transactions on Neural Networks7(4) (1996), 869–880.
29.
J.I.da Silva Filho, G.Lambert-Torres and J.M.Abe, Uncertainty Treatment using Paraconsistent Logic, IOS Press, 2010.
Gridded Population of the World, Version 4 (GPWv4): Population Density. NASA Socioeconomic Data and Applications Center (SEDAC), 2016. [Online]. Available: 10.7927/H4NP22DQ
