This work is licensed under a Creative Commons Attribution-NonCommercial-ShareAlike 4.0 International License.
Spatial Interpolation Long-Term Patterns Capacity of Solar Energy in Sumatera
Corresponding Author(s) : Arie Vatresia
Kinetik: Game Technology, Information System, Computer Network, Computing, Electronics, and Control,
Vol. 9, No. 3, August 2024
Abstract
Indonesia possesses considerable capacity for renewable energy as a result of its plentiful natural resources, including geothermal, solar, wind, hydro, and biomass. However, the nation's existing energy composition is predominantly dependent on non-renewable resources, with fossil fuels constituting approximately 95% of its overall energy consumption. Recently, Indonesia has made notable advancements in augmenting its renewable energy output in years. Nevertheless, there is still obscurity about the identification of suitable regions for the installation of solar power plants in order to facilitate the development of solar energy. This study employed a methodology to investigate and forecast the solar energy potential in Sumatra, Indonesia. The data utilized consists of MERRA-2 reanalyzing information spanning from 1980 to 2019, collected on a daily basis. The data is analyzed and shown using Inverse Distance Weighting and ARIMA techniques to visualize the spatial variation of solar energy potential in Sumatra. ARIMA is employed as a supplementary method to the interpolation technique in order to get long-term projections of solar energy potential for the period spanning from 2020 to 2029. The analysis of the best interpolation method for estimating solar energy potential reveals that the IDW approach with a power of 5 yields the most accurate findings, with an RMSE value of 28.33. For long-term prediction of solar potential in Aceh province, the ARIMA (1,0,0) method is recommended, which has a MAPE value of 0.0219. The findings indicated that Lampung and Bengkulu frequently experience the distribution of solar energy with an intensity ranging from 1400 to 1450 kWh. In addition, the forecast of the potential over Sumatera Island yielded encouraging results using the GAM model, with a root mean square error rate of 0.05103.
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- D. F. Silalahi, A. Blakers, M. Stocks, B. Lu, C. Cheng, and L. Hayes, “Indonesia’s vast solar energy potential,” Energies (Basel), vol. 14, no. 17, 2021. https://doi.org/10.3390/en14175424
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References
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D. Hartono, S. H. Hastuti, A. Halimatussadiah, A. Saraswati, A. F. Mita, and V. Indriani, “Comparing the impacts of fossil and renewable energy investments in Indonesia: A simple general equilibrium analysis,” Heliyon, vol. 6, no. 6, 2020. https://doi.org/10.1016/j.heliyon.2020.e04120
J. Langer, J. Quist, and K. Blok, “Review of renewable energy potentials in indonesia and their contribution to a 100% renewable electricity system,” Energies, vol. 14, no. 21. 2021. https://doi.org/10.3390/en14217033
M. I. al Irsyad, A. Halog, R. Nepal, and D. P. Koesrindartoto, “The Impacts of Emission Reduction Targets in Indonesia Electricity Systems,” Indonesian Journal of Energy, vol. 2, no. 2, 2019. https://doi.org/10.33116/ije.v2i2.42
S. Suhono, S. Sarjiya, and S. P. Hadi, “Electricity Demand and Supply Planning Analysis for Sumatera Interconnection System using Integrated Resources Planning Approach,” Jurnal Ilmiah Teknik Elektro Komputer dan Informatika, vol. 5, no. 1, 2019. http://dx.doi.org/10.26555/jiteki.v5i1.13178
Y. Sugiawan and S. Managi, “The environmental Kuznets curve in Indonesia: Exploring the potential of renewable energy,” Energy Policy, vol. 98, 2016. https://doi.org/10.1016/j.enpol.2016.08.029
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A. S. Ahmar, “A comparison of α-Sutte Indicator and ARIMA methods in renewable energy forecasting in Indonesia,” International Journal of Engineering and Technology(UAE), vol. 7, 2018. https://doi.org/10.14419/ijet.v7i1.6.12319
M. Anggraini and S. N. Indah, “IESR Efforts to Accelerate Indonesia Renewable Energy Transition Through Media Relations,” RSF Conference Series: Business, Management and Social Sciences, vol. 1, no. 4, 2021. https://doi.org/10.31098/bmss.v1i4.314
D. Akritidis, A. Pozzer, J. Flemming, A. Inness, and P. Zanis, “A Global Climatology of Tropopause Folds in CAMS and MERRA-2 Reanalyses,” Journal of Geophysical Research: Atmospheres, vol. 126, no. 8, 2021. https://doi.org/10.1029/2020JD034115
H. Kuswanto and A. Naufal, “Evaluation of performance of drought prediction in Indonesia based on TRMM and MERRA-2 using machine learning methods,” MethodsX, vol. 6, 2019. https://doi.org/10.1016/j.mex.2019.05.029
G. Y. Lu and D. W. Wong, “An adaptive inverse-distance weighting spatial interpolation technique,” Comput Geosci, vol. 34, no. 9, 2008. https://doi.org/10.1016/j.cageo.2007.07.010
Z. K. Barsi, I. L�szl�, G. Szab�, and H. M. Abdulmutalib, “Accuracy dimensions in remote sensing, International Archives of the Photogrammetry, Remote Sensing and Spatial Information Sciences - ISPRS Archives,” vol. 42, no. 3, pp. 61–67, 2018. https://doi.org/10.5194/isprs-archives-XLII-3-61-2018
S. Shekhar, M. R. Evans, J. M. Kang, and P. Mohan, “Identifying patterns in spatial information: A survey of methods,” Wiley Interdiscip Rev Data Min Knowl Discov, vol. 1, no. 3, pp. 193–214, 2011. https://doi.org/10.1002/widm.25
A. Bemporad, “Global optimization via inverse distance weighting and radial basis functions,” Comput Optim Appl, vol. 77, no. 2, 2020. https://doi.org/10.1007/s10589-020-00215-w
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D. F. Watson and G. M. Philip, “A Refinement of Inverse Distance Weighted Interpolation,” Geoprocessing, vol. 2, no. 4, pp. 315–327, 1985.
J. P. Musashi, H. Pramoedyo, and R. Fitriani, “Comparison of Inverse Distance Weighted and Natural Neighbor Interpolation Method at Air Temperature Data in Malang Region,” CAUCHY – JURNAL MATEMATIKA MURNI DAN APLIKASI, vol. 5, no. 2, pp. 48–54, 2018.
J. Fattah, L. Ezzine, Z. Aman, H. El Moussami, and A. Lachhab, “Forecasting of demand using ARIMA model,” International Journal of Engineering Business Management, vol. 10, 2018. https://doi.org/10.1177/1847979018808673
G. C. Tiao, “Time Series: ARIMA Methods,” in International Encyclopedia of the Social & Behavioral Sciences: Second Edition, 2015. https://doi.org/10.1016/B978-0-08-097086-8.42182-3
P. G. Zhang, “Time series forecasting using a hybrid ARIMA and neural network model,” Neurocomputing, vol. 50, 2003. https://doi.org/10.1016/S0925-2312(01)00702-0
E. Chodakowska, J. Nazarko, and Ł. Nazarko, “Arima models in electrical load forecasting and their robustness to noise,” Energies (Basel), vol. 14, no. 23, 2021. https://doi.org/10.3390/en14237952
C. H. Chang, S. Tan, B. Lengerich, A. Goldenberg, and R. Caruana, “How Interpretable and Trustworthy are GAMs?,” in Proceedings of the ACM SIGKDD International Conference on Knowledge Discovery and Data Mining, 2021. https://doi.org/10.1145/3447548.3467453
S. N. Wood, “Mixed GAM Computation Vehicle with Automatic Smoothness Estimation,” Generalized Additive Models: An Introduction with R, Second Edition, vol. 1.8-33, 2020.
S. N. Wood, “mgcv: GAMs and generalized ridge regression for R,” R News, vol. 1, 2001.
P. J. Mavares Ferrer, “Visualization of the Chaos Game for non-hyperbolic iterated function system,” REVISTA ODIGOS, vol. 1, no. 2, pp. 9–20, 2020. https://doi.org/10.35290/ro.v1n2.2020.302
D. Bera, N. Das Chatterjee, and S. Bera, “Comparative performance of linear regression, polynomial regression and generalized additive model for canopy cover estimation in the dry deciduous forest of West Bengal, Remote Sensing Applications: Society and Environment,” vol. 22, p. 100502, Dec. 2021. https://doi.org/10.1016/j.rsase.2021.100502
E. J. Pedersen, D. L. Miller, G. L. Simpson, and N. Ross, “Hierarchical generalized additive models in ecology: An introduction with mgcv, PeerJ,” vol. 2019, no. 5, pp. 1–42, 2019. https://doi.org/10.7717/peerj.6876
A. Deniz and Y. Özdemir, “Graph-directed fractal interpolation functions,” Turkish Journal of Mathematics, vol. 41, no. 4, 2017. https://doi.org/10.3906/mat-1604-39
V. Chaplot, F. Darboux, H. Bourennane, S. Leguédois, N. Silvera, and K. Phachomphon, “Accuracy of interpolation techniques for the derivation of digital elevation models in relation to landform types and data density,” Geomorphology, vol. 77, pp. 126–141, 2006. https://doi.org/10.1016/j.geomorph.2005.12.010
G. Gertner, G. Wang, S. Fang, and A. B. Anderson, “Mapping and uncertainty of predictions based on multiple primary variables from joint co-simulation with Landsat TM image and polynomial regression,” Remote Sens Environ, vol. 83, no. 3, pp. 498–510, 2002. https://doi.org/10.1016/S0034-4257(02)00066-4
A. Terlizzi, D. Scuderi, S. Fraschetti, and M. J. Anderson, “Quantifying effects of pollution on biodiversity: A case study of highly diverse molluscan assemblages in the Mediterranean,” Mar Biol, vol. 148, no. 2, pp. 293–305, 2005. https://doi.org/10.1007/s00227-005-0080-8
H. Y. Wang, S. Z. Yang, and X. J. Li, “Error analysis for bivariate fractal interpolation functions generated by 3-D perturbed iterated function systems,” Computers and Mathematics with Applications, vol. 56, no. 7, 2008. https://doi.org/10.1016/j.camwa.2008.03.026
C. C. Nwokike and E. W. Okereke, “Comparison of the Performance of the SANN, SARIMA and ARIMA Models for Forecasting Quarterly GDP of Nigeria,” Asian Research Journal of Mathematics, 2021. https://doi.org/10.9734/arjom/2021/v17i330280