This study aimed to create a Malaysian wind map of greater accuracy. Compared to a previous wind map, spatial modeling input was increased. The Genetic Algorithm-optimized Artificial Neural Network Measure–Correlate–Predict method was used to impute missing data, and managed to control over- or under-prediction issues. The established wind map was made more reliable by including surface roughness to simulate wind flow over complex terrain. Validation revealed that the current wind map is 33.833% more accurate than the previous wind map. Furthermore, the correlation coefficient between wind map-simulated data and observed data was high as 0.835. In conclusion, the new and improved wind map for Malaysia simulates data with acceptable accuracy.
AddisonJFDHunterABassJ, et al. (2000) A neural network version of the measure correlate predict algorithm for estimating wind energy yield. In: Proceedings of 13th International Congress on Condition Monitoring and Diagnostic Engineering, pp. 917–922. Available at: https://core.ac.uk/download/pdf/53912.pdf
2.
AhmedS (2011) Wind Energy: Theory and Practice, 2nd edn.Delhi, India: PHI.
3.
AnwarzaiMANagasakaK (2017) Utility-scale implementable potential of wind and solar energies for Afghanistan using GIS multi-criteria decision analysis. Renewable and Sustainable Energy Reviews71: 150–160.
4.
AWS Scientific, Inc. (1997). Wind Resource Assessment Handbook. New York: National Renewable Energy Laboratory.
5.
BadelMAngoraniSShariat PanahiM (2011) The application of median indicator kriging and neural network in modeling mixed population in an iron ore deposit. Computational Geosciences37(4): 530–540.
6.
BandocGPrăvălieRPatricheC, et al. (2018) Spatial assessment of wind power potential at global scale: A geographical approach. Journal of Cleaner Production200: 1065–1086.
7.
Bañuelos-RuedasFAngeles-CamachoCRios-MarcuelloS (2010) Analysis and validation of the methodology used in the extrapolation of wind speed data at different heights. Renewable and Sustainable Energy Reviews14(8): 2383–2391.
8.
BassyouniM.GutubS.JavaidU., et al. (2015). Assessment and analysis of wind power resource using weibull parameters. Energy Exploration and Exploitation, 33(1), 105–122. https://doi.org/10.1260/0144-5987.33.1.105
9.
BontempsS.DefournyP.BogaertE., et al. (2011). GlobCover 2009 Products Description and Validation Report. Belgium: UCLouvain & ESA Team.
10.
BostanPAHeuvelinkGBAkyurekSZ (2012) Comparison of regression and kriging techniques for mapping the average annual precipitation of Turkey. International Journal of Applied Earth Observation and Geoinformation19: 115–126.
11.
CartaJAVelázquezSCabreraP (2013) A review of measure-correlate-predict (MCP) methods used to estimate long-term wind characteristics at a target site. Renewable and Sustainable Energy Reviews27: 362–400.
12.
CelluraMCirrincioneGMarvugliaA, et al. (2008) Wind speed spatial estimation for energy planning in Sicily: Introduction and statistical analysis. Renewable Energy33(6): 1237–1250.
ElliottD.SchwartzM.GeorgeR., et al. (2001). Wind Energy Resource Atlas of the Philippines. Cole Boulevard: National Renewable Energy Laboratory.
15.
FrankHPLandbergL (1997) Modelling the wind climate of Ireland. Boundary-Layer Meteorology85(3): 359–377.
16.
FrankHPRathmannOMortensenNG, et al. (2001) The Numerical Wind Atlas – KAMM/W AsP Method. Roskilde, Denmark: Forskningscenter Risoe.
17.
GhorbaniBZiabasharhaghMAmidpourM (2014) A hybrid artificial neural network and genetic algorithm for predicting viscosity of Iranian crude oils. Journal of Natural Gas Science and Engineering18: 312–323.
18.
GualtieriGSecciS (2012) Methods to extrapolate wind resource to the turbine hub height based on power law: A 1-h wind speed vs. Weibull distribution extrapolation comparison. Renewable Energy43: 183–200.
19.
GuoZChiDWuJ, et al. (2014) A new wind speed forecasting strategy based on the chaotic time series modelling technique and the Apriori algorithm. Energy Conversion and Management84: 140–151.
20.
IbrahimMZHwangYKIsmailM, et al. (2014) Wind speed modeling for Malaysia. International Journal of Renewable Energy Research4(4): 912–923.
21.
KubikMLCokerPJHuntC (2011) Using Meteorological Wind Data to Estimate Turbine Generation Output: A Sensitivity Analysis. World Renewable Energy Congress 2011 - Sweden (pp. 4074–4081). Linköping: Wind Energy Application (WE).
22.
LawanSMZainul AbidinWAWYin ChaiW, et al. (2014) Development of wind mapping based on artificial neural network (ANN) for energy exploration in Sarawak. International Journal of Renewable Energy Research4(3). Available at: http://ijrer.org/index.php/ijrer/article/view/1377 (accessed 29 September 2014).
23.
LiGShiJ (2010) On comparing three artificial neural networks for wind speed forecasting. Applied Energy87(7): 2313–2320.
24.
LiJHeapAD (2011) A review of comparative studies of spatial interpolation methods in environmental sciences: Performance and impact factors. Ecological Informatics6(3–4): 228–241.
ManwellJMcGowanJRogersA (2009) Wind Energy Explained Theory, Design and Application, 2nd edn. United Kingdom: John Wiley & Sons.
29.
McCallJ (2005) Genetic algorithms for modelling and optimisation. Journal of Computational and Applied Mathematics184(1): 205–222.
30.
MehrpooyaMGhorbaniBJafariB, et al. (2018) Modeling of a single cell micro proton exchange membrane fuel cell by a new hybrid neural network method. Thermal Science and Engineering Progress7: 8–19.
31.
Mohammadzadeh BinaSJalilinasrabadySFujiiH, et al. (2018) A comprehensive approach for wind power plant potential assessment, application to northwestern Iran. Energy164: 344–358.
32.
MoreADeoMC (2003) Forecasting wind with neural networks. Marine Structures16(1): 35–49.
33.
MuzathikAMNikWBWIbrahimMZ, et al. (2009) Wind resource investigation of Terengganu in the west Malaysia. Wind Engineering33(4): 389–402.
ProbstOCárdenasD (2010) State of the art and trends in wind resource assessment. Energies3(6): 1087–1141.
36.
RehmanSNatarajanNVasudevanM, et al. (2020) Assessment of wind energy potential across varying topographical features of Tamil Nadu, India. Energy Exploration & Exploitation38(1): 175–200.
37.
SahaSMoorthiSPanH-L, et al. (2010) The NCEP climate forecast system reanalysis. Bulletin of the American Meteorological Society91(8): 1015–1058.
38.
ShirmohammadiRGhorbaniBHamediM, et al. (2015) Optimization of mixed refrigerant systems in low temperature applications by means of group method of data handling (GMDH). Journal of Natural Gas Science and Engineering26: 303–312.
39.
SperaDA (2009) Wind Turbine Technology: Fundamental Concepts of Wind Turbine Engineering, 2nd edn.New York, NY: ASME Press.
40.
SperaDARichardsTR (1979) Modified power law equations for vertical wind profiles. Wind Characteristics and Wind Energy Siting Conference. Oregon: U. S. Department of Energy.
41.
SreevalsanESiddarth ShankarDSasi KumarR, et al. (2010) Indian Wind Atlas. Chennai, India: Centre for Wind Energy Technology (C-WET).
42.
TarK (2008) Some statistical characteristics of monthly average wind speed at various heights. Renewable and Sustainable Energy Reviews12(6): 1712–1724.
43.
TieoJJSkoteMSrikanthN (2020) Suitability of power-law extrapolation for wind speed estimation on a tropical island. Journal of Wind Engineering and Industrial Aerodynamics205: 104317.
44.
TroenILundtang PetersenE (1989) European Wind Atlas. Roskilde: Riso National Laboratory.
45.
VelázquezSCartaJAMatíasJM (2011) Comparison between ANNs and linear MCP algorithms in the long-term estimation of the cost per kWh produced by a wind turbine at a candidate site: A case study in the Canary Islands. Applied Energy88(11): 3869–3881.
46.
WaewsakJLandryMGagnonY (2013) High resolution wind atlas for Nakhon Si Thammarat and Songkhla provinces, Thailand. Renewable Energy53: 101–110.
47.
WalpoleREMyersRHMyersSL, et al. (2012) Probability and Statistics for Engineers and Scientists (9th edn.). United States of America: Pearson Education.
