-
1
-
-
85027930799
-
Fuzzy prediction interval models for forecasting renewable resources and loads in microgrids
-
March
-
[1] Saez, D., Avila, F., Olivares, D., Canizares, C., Fuzzy prediction interval models for forecasting renewable resources and loads in microgrids. IEEE Trans Smart Grid 6:2 (March 2015), 548–556.
-
(2015)
IEEE Trans Smart Grid
, vol.6
, Issue.2
, pp. 548-556
-
-
Saez, D.1
Avila, F.2
Olivares, D.3
Canizares, C.4
-
2
-
-
84946021085
-
A hybrid wind speed forecasting model based on phase space reconstruction theory and Markov model: a case study of wind farms in northwest China
-
[2] Wang, J.Z., Hua, J.M., A hybrid wind speed forecasting model based on phase space reconstruction theory and Markov model: a case study of wind farms in northwest China. Energy 39 (Nov. 2015), 556–572.
-
(2015)
Energy
, vol.39
, pp. 556-572
-
-
Wang, J.Z.1
Hua, J.M.2
-
3
-
-
84928429252
-
Short-term solar irradiation forecasting based on dynamic harmonic regression original Research article
-
May
-
[3] Trapero, J.R., Kourentzes, N., Martin, A., Short-term solar irradiation forecasting based on dynamic harmonic regression original Research article. Energy 84 (May 2015), 289–295.
-
(2015)
Energy
, vol.84
, pp. 289-295
-
-
Trapero, J.R.1
Kourentzes, N.2
Martin, A.3
-
4
-
-
77956084111
-
Monthly and seasonal assessment of wind energy characteristics at four monitored locations in Liguria region (Italy)
-
[4] Ouammi, A., Dagdougui, H., Sacile, R., Mimet, A., Monthly and seasonal assessment of wind energy characteristics at four monitored locations in Liguria region (Italy). Renew Sustain Energy Rev 14:7 (Sep. 2010), 1959–1968.
-
(2010)
Renew Sustain Energy Rev
, vol.14
, Issue.7
, pp. 1959-1968
-
-
Ouammi, A.1
Dagdougui, H.2
Sacile, R.3
Mimet, A.4
-
5
-
-
84908152186
-
A new hybrid evolutionary based RBF networks method for forecasting time series: a case study of forecasting emergency supply demand time series
-
[5] Mohammadi, R., Fatemi Ghomi, S.M.T., Zeinali, F., A new hybrid evolutionary based RBF networks method for forecasting time series: a case study of forecasting emergency supply demand time series. Eng Appl Artif Intell 36 (Nov. 2014), 204–214.
-
(2014)
Eng Appl Artif Intell
, vol.36
, pp. 204-214
-
-
Mohammadi, R.1
Fatemi Ghomi, S.M.T.2
Zeinali, F.3
-
6
-
-
84940040929
-
Kalman filter-based method for online sequential extreme learning machine for regression problems
-
[6] Nobrega, J.P., Oliveira, A.L.I., Kalman filter-based method for online sequential extreme learning machine for regression problems. Eng Appl Artif Intell 44 (Sep. 2015), 101–110.
-
(2015)
Eng Appl Artif Intell
, vol.44
, pp. 101-110
-
-
Nobrega, J.P.1
Oliveira, A.L.I.2
-
7
-
-
84916879761
-
Short term load forecast using fuzzy logic and wavelet transform integrated generalized neural network
-
[7] Chaturvedi, D.K., Sinha, A.P., Malik, O.P., Short term load forecast using fuzzy logic and wavelet transform integrated generalized neural network. Int J Electr Power & Energy Syst 67 (May. 2015), 230–237.
-
(2015)
Int J Electr Power & Energy Syst
, vol.67
, pp. 230-237
-
-
Chaturvedi, D.K.1
Sinha, A.P.2
Malik, O.P.3
-
8
-
-
84922351856
-
A new method for short-term load forecasting based on fractal interpretation and wavelet analysis
-
July
-
[8] Zhai, M.Y., A new method for short-term load forecasting based on fractal interpretation and wavelet analysis. Int J Electr Power & Energy Syst 69 (July 2015), 241–245.
-
(2015)
Int J Electr Power & Energy Syst
, vol.69
, pp. 241-245
-
-
Zhai, M.Y.1
-
9
-
-
84892569845
-
Short-term wind power ensemble prediction based on Gaussian processes and neural networks
-
[9] Lee, Duehee, Baldick, R., Short-term wind power ensemble prediction based on Gaussian processes and neural networks. IEEE Trans Smart Grid 5:1 (Jan. 2014), 501–510.
-
(2014)
IEEE Trans Smart Grid
, vol.5
, Issue.1
, pp. 501-510
-
-
Lee, D.1
Baldick, R.2
-
10
-
-
33750297514
-
Short-term ANN load forecasting from limited data using generalization learning strategies
-
[10] Zeke, S.H., Chana, H.W., Nganb, A.B., Radb, A.K., Davidb, Kasabova, N., Short-term ANN load forecasting from limited data using generalization learning strategies. Neurocomputing 70 (2006), 409–419.
-
(2006)
Neurocomputing
, vol.70
, pp. 409-419
-
-
Zeke, S.H.1
Chana, H.W.2
Nganb, A.B.3
Radb, A.K.4
Davidb5
Kasabova, N.6
-
11
-
-
9244240793
-
Load forecasting using support vector machines: a study on EUNITE competition 2001
-
[11] Chen, B.J., Chang, M.W., Lin, C.J., Load forecasting using support vector machines: a study on EUNITE competition 2001. IEEE Trans Power Syst 19:4 (Nov. 2004), 1821–1830.
-
(2004)
IEEE Trans Power Syst
, vol.19
, Issue.4
, pp. 1821-1830
-
-
Chen, B.J.1
Chang, M.W.2
Lin, C.J.3
-
12
-
-
77955516396
-
Finding optimal model parameters by deterministic and annealed focused grid search
-
[12] Jiménez, Á.B., Lázaro, J.L., Dorronsoro, J.R., Finding optimal model parameters by deterministic and annealed focused grid search. Neurocomputing 72:1 (Aug. 2008), 2824–2832.
-
(2008)
Neurocomputing
, vol.72
, Issue.1
, pp. 2824-2832
-
-
Jiménez, Á.B.1
Lázaro, J.L.2
Dorronsoro, J.R.3
-
13
-
-
84928987064
-
Towards reactive power dispatch within a wind farm using hybrid PSO
-
[13] Kumar, A., Tsvetkov, P.V., Towards reactive power dispatch within a wind farm using hybrid PSO. Ann Nucl Energy 85 (Nov. 2015), 27–35.
-
(2015)
Ann Nucl Energy
, vol.85
, pp. 27-35
-
-
Kumar, A.1
Tsvetkov, P.V.2
-
14
-
-
84922341864
-
Towards reactive power dispatch within a wind farm using hybrid PSO
-
[14] Kanna, B., Singh, S.N., Towards reactive power dispatch within a wind farm using hybrid PSO. Int J Electr Power & Energy Syst 69 (Jul. 2015), 232–240.
-
(2015)
Int J Electr Power & Energy Syst
, vol.69
, pp. 232-240
-
-
Kanna, B.1
Singh, S.N.2
-
15
-
-
84871719024
-
An energy management system (EMS) strategy for combined heat and power (CHP) systems based on a hybrid optimization method employing fuzzy programming original research article
-
January
-
[15] Moradi, MoH., Hajinazari, M., Jamasb, S., Paripour, M., An energy management system (EMS) strategy for combined heat and power (CHP) systems based on a hybrid optimization method employing fuzzy programming original research article. Energy 49 (January 2013), 86–101.
-
(2013)
Energy
, vol.49
, pp. 86-101
-
-
Moradi, M.1
Hajinazari, M.2
Jamasb, S.3
Paripour, M.4
-
16
-
-
84949681323
-
A robust combination approach for short-term wind speed forecasting and analysis – combination of the ARIMA (autoregressive integrated moving average), ELM (extreme learning machine), SVM (support vector machine) and LSSVM (least square SVM) forecasts using a GPR (Gaussian process regression) model
-
[16] Wang, J.Z., Hua, J.M., A robust combination approach for short-term wind speed forecasting and analysis – combination of the ARIMA (autoregressive integrated moving average), ELM (extreme learning machine), SVM (support vector machine) and LSSVM (least square SVM) forecasts using a GPR (Gaussian process regression) model. Energy 45 (Aug.2015), 41–56.
-
(2015)
Energy
, vol.45
, pp. 41-56
-
-
Wang, J.Z.1
Hua, J.M.2
-
17
-
-
84918801543
-
Hybridization of seasonal chaotic cloud simulated annealing algorithm in a SVR-based load forecasting model
-
[17] Geng, J., Huang, M.L., Li, M.W., Hong, W.C., Hybridization of seasonal chaotic cloud simulated annealing algorithm in a SVR-based load forecasting model. Neurocomputing 151 (Mar. 2015), 1362–1373.
-
(2015)
Neurocomputing
, vol.151
, pp. 1362-1373
-
-
Geng, J.1
Huang, M.L.2
Li, M.W.3
Hong, W.C.4
-
18
-
-
84896951924
-
Hybrid of ARIMA and SVMs for short-term load ForecastingOriginal Research article
-
[18] Nie, H.Z., Liu, G.H., Liu, X.M., Wang, Y., Hybrid of ARIMA and SVMs for short-term load ForecastingOriginal Research article. Energy Proced. 16 (Jan, 2012), 1455–1460.
-
(2012)
Energy Proced.
, vol.16
, pp. 1455-1460
-
-
Nie, H.Z.1
Liu, G.H.2
Liu, X.M.3
Wang, Y.4
-
19
-
-
84956634384
-
Design of experiments and focused grid search for neural network parameter optimization
-
[19] Pontesa, F.J., Amorimb, G.F., Balestrassib, P.P., Paivab, A.P., Ferreirad, J.R., Design of experiments and focused grid search for neural network parameter optimization. Neurocomputing 186 (Jan. 2016), 22–34.
-
(2016)
Neurocomputing
, vol.186
, pp. 22-34
-
-
Pontesa, F.J.1
Amorimb, G.F.2
Balestrassib, P.P.3
Paivab, A.P.4
Ferreirad, J.R.5
-
20
-
-
84947252235
-
A hybrid PSO-GA algorithm for constrained optimization problems
-
[20] Garg, H., A hybrid PSO-GA algorithm for constrained optimization problems. Article Appl Math Comput 274 (Feb. 2016), 292–305.
-
(2016)
Article Appl Math Comput
, vol.274
, pp. 292-305
-
-
Garg, H.1
-
21
-
-
84949267100
-
ANN–GA smart appliance scheduling for optimized energy management in the domestic sector
-
[21] Yuce, Baris, Rezgui, Yacine, Mourshed, Monjur, ANN–GA smart appliance scheduling for optimized energy management in the domestic sector. Energy Build 111 (Jan. 2016), 311–325.
-
(2016)
Energy Build
, vol.111
, pp. 311-325
-
-
Yuce, B.1
Rezgui, Y.2
Mourshed, M.3
-
22
-
-
84884126948
-
Short-term wind speed forecasting using wavelet transform and support vector machines optimized by genetic algorithm
-
[22] Liu, D., Niu, D., Wang, H., Fan, L., Short-term wind speed forecasting using wavelet transform and support vector machines optimized by genetic algorithm. Renew Energy 62 (Feb. 2014), 592–597.
-
(2014)
Renew Energy
, vol.62
, pp. 592-597
-
-
Liu, D.1
Niu, D.2
Wang, H.3
Fan, L.4
-
23
-
-
63349102517
-
Parameter selection for support vector machines based on genetic algorithms to short-term power load forecasting
-
[23] Wu, J.L., Yang, S.X., Liu, C.S., Parameter selection for support vector machines based on genetic algorithms to short-term power load forecasting. J Central South Univ Sci Technol 40:1 (Feb. 2009), 180–184.
-
(2009)
J Central South Univ Sci Technol
, vol.40
, Issue.1
, pp. 180-184
-
-
Wu, J.L.1
Yang, S.X.2
Liu, C.S.3
-
24
-
-
84931575306
-
Predicting the wind power density based upon extreme learning machine
-
[24] Mohammadi, K., Shamshirband, S., Yee, Por Lip, Petkovic, D., Zamani, M., Sudheer, Ch, Predicting the wind power density based upon extreme learning machine. Energy 86:15 (Jun. 2015), 232–239.
-
(2015)
Energy
, vol.86
, Issue.15
, pp. 232-239
-
-
Mohammadi, K.1
Shamshirband, S.2
Yee, P.L.3
Petkovic, D.4
Zamani, M.5
Sudheer, C.6
-
25
-
-
85043751154
-
-
Data source, available:.
-
[25] Data source, available: http://www.elia.be/en/griddata/power-generation.
-
-
-
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