1 |
A. N. Sekhar, K. S. Rajan and A. Jain, "Spatial informatics and geographical information systems: tools to transform electric power and energy systems," in TENCON 2008-2008 IEEE Region 10 Conference, pp. 1-5, 2008.
|
2 |
P. B. T. Inc., "Interactive city map of electricity, "http://www.powermap.com.cn/, 2016-08-26
|
3 |
X. Bai, Z. Chao and M. U. Gang, "Review and prospect of the spatial load forecasting methods," Proceedings of the CSEE, vol. 33, no. 25, pp. 78-92, 2013.
|
4 |
X. Bai, G. Peng-wei, M. Gang, Y. Gan-gui, L. Ping, C. Hong-wei, L. Jie-fu, and B. Yang, "A spatial load forecasting method based on the theory of clustering analysis," Physics Procedia, vol. 24, Part A, pp. 176- 183, 2012.
DOI
|
5 |
J. D. Melo, E. M. Carreno and A. Padilha-Feltrin, "Multi-agent simulation of urban social dynamics for spatial load forecasting," IEEE Transactions on Power Systems, vol. 27, no. 4, pp. 1870-1878, 2012.
DOI
|
6 |
J. D. Melo, E. M. Carreno, A. Padilha Feltrin, and C. R. Minussi, "Grid‐based simulation method for spatial electric load forecasting using power‐law distribution with fractal exponent," International Transactions on Electrical Energy Systems, 2015.
|
7 |
Monika, D. Srinivasan and T. Reindl, "Real-time display of data from a smart-grid on geographical map using a GIS tool and its role in optimization of game theory," in Smart Grid Technologies - Asia,, pp. 1-6, 2015.
|
8 |
J. Yu, F. Yan, W. Yang, and X. Gao, "Spatial load forecasting of distribution network based on fuzzy multi-objective multi-person decision making," Power System Technology, vol. 30, no. 7, pp. 69-76, 2006.
|
9 |
X. He, Q. Ai, R. C. Qiu, J. Ni, L. Piao, Y. Xu, and X. Xu, "3D Power-map for smart grids - An integration of high-dimensional analysis and visualization," Statistics, 2015.
|
10 |
E. M. Carreno, R. M. Rocha and A. Padilha-Feltrin, "A cellular automaton approach to spatial electric load forecasting," Power Systems, IEEE Transactions on, vol. 26, no. 2, pp. 532-540, 2011.
|
11 |
S. Lei, C. Sun, Q. Zhou, and X. Zhang, "Application of fuzzy rough set theory in spatial load forecasting," Power System Technology, vol. 29, no. 9, pp. 26-30, 2005.
|
12 |
L. J. Liu, Y. Fu, S. W. Ma, and R. Hu, "Spatial load forecasting of distribution network based on intuitionistic fuzzy entropy and fuzzy clustering," Advanced Materials Research, vol. 516-517, no. 100, pp. 1433-1436, 2012.
DOI
|
13 |
X. Yang, J. Yuan, T. Zhang, and H. Mao, "Application of uncertainty reasoning based on cloud theory in spatial load forecasting," in World Congress on Intelligent Control and Automation, pp. 7567 - 7571, 2006.
|
14 |
W. B. Tao, L. Z. Zhang, P. Hong, L. I. Zhen-Yuan, and Z. Hua, "Spatial electric load forecasting based on double-level bayesian classification," Proceedings of the CSEE, 2007.
|
15 |
Z. Quan, S. Wei, H. Ren, Z. Yun, C. Sun, G. Xie, and J. Deng, "Spatial load forecasting of distribution network based on least squares support vector machine and load density index system," Power System Technology, vol. 35, no. 1, pp. 66-71, 2011.
|
16 |
Z. Sun, X. Wang, Z. Shouxiang, W. Lei, and S. Guo, "New load density forecasting method for objective network planning," in International Conference on MEMS NANO, and Smart Systems, pp. 114-117, 2009.
|
17 |
C. H. Jin, G. Pok, Y. Lee, H. Park, K. D. Kim, U. Yun, and K. H. Ryu, "A SOM clustering pattern sequence-based next symbol prediction method for day-ahead direct electricity load and price forecasting," Energy Conversion and Management, vol. 90, pp. 84-92, 2015.
DOI
|
18 |
M. Ghofrani, M. Ghayekhloo, A. Arabali, and A. Ghayekhloo, "A hybrid short-term load forecasting with a new input selection framework," Energy, vol. 81, pp. 777-786, 2015.
DOI
|
19 |
S. Li, P. Wang and L. Goel, "Short-term load forecasting by wavelet transform and evolutionary extreme learning machine," Electric Power Systems Research, vol. 122, pp. 96-103, 2015.
DOI
|
20 |
J. D. Melo, E. M. Carreno, A. Calvino, and A. Padilha-Feltrin, "Determining spatial resolution in spatial load forecasting using a grid-based model," Electric Power Systems Research, vol. 111, pp. 177- 184, 2014.
DOI
|
21 |
F. L. Quilumba, W. Lee, H. Huang, D. Y. Wang, and R. L. Szabados, "Using smart meter data to improve the accuracy of intraday load forecasting considering customer behavior similarities," IEEE Transactions on Smart Grid, vol. 6, no. 2, pp. 911-918, 2015.
DOI
|
22 |
C. Wang, G. Grozev and S. Seo, "Decomposition and statistical analysis for regional electricity demand forecasting," Energy, vol. 41, no. 1, pp. 313-325, 2012.
DOI
|
23 |
A. S. Khwaja, M. Naeem, A. Anpalagan, A. Venetsanopoulos, and B. Venkatesh, "Improved short-term load forecasting using bagged neural networks," Electric Power Systems Research, vol. 125, pp. 109-115, 2015.
DOI
|
24 |
S. Bandyopadhyay, T. Ganu, H. Khadilkar, and V. Arya, "Individual and aggregate electrical load forecasting: one for all and all for one," in ACM Sixth International Conference, pp. 1653-1654, 2015.
|
25 |
X. M. Yang, J. S. Yuan, J. F. Wang, and X. Gao, "A new spatial forecasting method for distribution network based on cloud theory," Proceedings of the CSEE, vol. 26, no. 6, pp. 30-36, 2006.
|
26 |
M. Batty, Cities and complexity: understanding cities with cellular automata, agent-based models, and fractals: The MIT press, pp. 1-565, 2007.
|
27 |
A. Khosravi, S. Nahavandi and D. Creighton, "Load forecasting and neural networks: A prediction interval-based perspective," in Computational intelligence in power engineering: Springer, pp. 131-150, 2010.
|
28 |
B. Xiao, P. Nie, G. Mu, J. Wang, and L. Tian, "A spatial load forecasting method based on multilevel clustering analysis and support vector machine," Automation of Electric Power Systems, vol. 39, no. 12, pp. 56-61, 2015.
|
29 |
R. Lleti, M. C. Ortiz, L. A. Sarabia, and M. S. Sanchez, "Selecting variables for k-means cluster analysis by using a genetic algorithm that optimises the silhouettes," Analytica Chimica Acta, vol. 515, no. 1, pp. 87-100, 2004.
DOI
|
30 |
A. Rajaraman, J. Leskovec and J. D. Ullman, Mining of massive datasets, 1 ed. Cambridge, United Kingdom: Cambridge University Press, pp. 253-254, 2011.
|
31 |
J. W. Taylor and R. Buizza, "Neural network load forecasting with weather ensemble predictions," IEEE Transactions on Power Systems, vol. 17, no. 3, pp. 626-632, 2002.
DOI
|
32 |
A. Khosravi, S. Nahavandi and D. Creighton, "Construction of optimal prediction intervals for load forecasting problems," IEEE Transactions on Power Systems, vol. 25, no. 3, pp. 1496-1503, 2010.
DOI
|
33 |
A. Khosravi, S. Nahavandi and D. Creighton, "Prediction interval construction and optimization for adaptive neurofuzzy inference systems," IEEE Transactions on Fuzzy Systems, vol. 19, no. 5, pp. 983-988, 2011.
DOI
|
34 |
N. A. A. Jalil, M. H. Ahmad and N. Mohamed, "Electricity load demand forecasting using exponential smoothing methods," World Applied Sciences Journal, vol. 22, no. 11, pp. 1540-1543, 2013.
|
35 |
A. J. Coale, P. Demeny and B. Vaughan, Regional Model Life Tables and Stable Populations: Studies in Population, 2 ed. New York, USA: ACADEMIC PRESS, pp. 25-36, 2013.
|
36 |
A. W. L. Yao, S. C. Chi and J. H. Chen, "An improved Grey-based approach for electricity demand forecasting," Electric Power Systems Research, vol. 67, no. 3, pp. 217-224, 2003.
DOI
|
37 |
G. I. Treyz, Regional economic modeling: A systematic approach to economic forecasting and policy analysis. Berlin, Germany: Springer Science & Business Media, pp. 258-262, 2013.
|
38 |
D. B. Stephenson, C. Coelho, F. J. DOBLAS REYES, and M. Balmaseda, "Forecast assimilation: a unified framework for the combination of multi - model weather and climate predictions," Tellus A, vol. 57, no. 3, pp. 253-264, 2005.
DOI
|