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Spatial Interpolation Long-Term Patterns Capacity of Renewable 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 (Article in Progress)
Abstract
Indonesia possesses considerable capacity for renewable energy as a result of its plentiful natural resources, including geothermal, solar, wind, hydro, and biomass. Nevertheless, the nation's existing energy composition is predominantly dependent on non-renewable resources, with fossil fuels constituting approximately 95% of its overall energy consumption. Nevertheless, Indonesia has made notable advancements in augmenting its renewable energy output in recent years. Nevertheless, there is still a lack of clarity about the identification of suitable regions for the installation of solar power plants (PLTS) 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 IDW (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 (RSME) rate of 0.05103.
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- Ahmar, A. S. (2018). A comparison of α-Sutte Indicator and ARIMA methods in renewable energy forecasting in Indonesia. International Journal of Engineering and Technology(UAE), 7. https://doi.org/10.14419/ijet.v7i1.6.12319
- Akritidis, D., Pozzer, A., Flemming, J., Inness, A., & Zanis, P. (2021). A Global Climatology of Tropopause Folds in CAMS and MERRA-2 Reanalyses. Journal of Geophysical Research: Atmospheres, 126(8). https://doi.org/10.1029/2020JD034115
- Anggraini, M., & Indah, S. N. (2021). IESR Efforts to Accelerate Indonesia Renewable Energy Transition Through Media Relations. RSF Conference Series: Business, Management and Social Sciences, 1(4). https://doi.org/10.31098/bmss.v1i4.314
- Barsi, Z. K., L�szl�, I., Szab�, G., & Abdulmutalib, H. M. (2018). Accuracy dimensions in remote sensing, International Archives of the Photogrammetry, Remote Sensing and Spatial Information Sciences - ISPRS Archives. 42(3), 61–67.
- Bemporad, A. (2020). Global optimization via inverse distance weighting and radial basis functions. Computational Optimization and Applications, 77(2). https://doi.org/10.1007/s10589-020-00215-w
- Bera, D., Chatterjee, N. das, & Bera, S. (2021). 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. 22, 100502.
- Chang, C. H., Tan, S., Lengerich, B., Goldenberg, A., & Caruana, R. (2021). How Interpretable and Trustworthy are GAMs? Proceedings of the ACM SIGKDD International Conference on Knowledge Discovery and Data Mining. https://doi.org/10.1145/3447548.3467453
- Chodakowska, E., Nazarko, J., & Nazarko, Ł. (2021). Arima models in electrical load forecasting and their robustness to noise. Energies, 14(23). https://doi.org/10.3390/en14237952
- Fattah, J., Ezzine, L., Aman, Z., el Moussami, H., & Lachhab, A. (2018). Forecasting of demand using ARIMA model. International Journal of Engineering Business Management, 10. https://doi.org/10.1177/1847979018808673
- Hartono, D., Hastuti, S. H., Halimatussadiah, A., Saraswati, A., Mita, A. F., & Indriani, V. (2020). Comparing the impacts of fossil and renewable energy investments in Indonesia: A simple general equilibrium analysis. Heliyon, 6(6). https://doi.org/10.1016/j.heliyon.2020.e04120
- Hidayatno, A., Dhamayanti, R., & Destyanto, A. R. (2019). Model conceptualization for policy analysis in renewable energy development in Indonesia by using system dynamics. International Journal of Smart Grid and Clean Energy, 8(1). https://doi.org/10.12720/sgce.8.1.54-58
- Irsyad, M. I. al, Halog, A., Nepal, R., & Koesrindartoto, D. P. (2019). The Impacts of Emission Reduction Targets in Indonesia Electricity Systems. Indonesian Journal of Energy, 2(2). https://doi.org/10.33116/ije.v2i2.42
- Kuswanto, H., & Naufal, A. (2019). Evaluation of performance of drought prediction in Indonesia based on TRMM and MERRA-2 using machine learning methods. MethodsX, 6. https://doi.org/10.1016/j.mex.2019.05.029
- Langer, J., Quist, J., & Blok, K. (2021). Review of renewable energy potentials in indonesia and their contribution to a 100% renewable electricity system. In Energies (Vol. 14, Issue 21). https://doi.org/10.3390/en14217033
- Li, Z., Zhang, X., Zhu, R., Zhang, Z., & Weng, Z. (2020). Integrating data-to-data correlation into inverse distance weighting. Computational Geosciences, 24(1). https://doi.org/10.1007/s10596-019-09913-9
- Lu, G. Y., & Wong, D. W. (2008). An adaptive inverse-distance weighting spatial interpolation technique. Computers and Geosciences, 34(9). https://doi.org/10.1016/j.cageo.2007.07.010
- Musashi, J. P., Pramoedyo, H., & Fitriani, R. (2018). Comparison of Inverse Distance Weighted and Natural Neighbor Interpolation Method at Air Temperature Data in Malang Region. CAUCHY – JURNAL MATEMATIKA MURNI DAN APLIKASI, 5(2), 48–54.
- Pedersen, E. J., Miller, D. L., Simpson, G. L., & Ross, N. (2019). Hierarchical generalized additive models in ecology: An introduction with mgcv. PeerJ, 2019(5). https://doi.org/10.7717/peerj.6876
- Shekhar, S., Evans, M. R., Kang, J. M., & Mohan, P. (2011). Identifying patterns in spatial information: A survey of methods. Wiley Interdisciplinary Reviews: Data Mining and Knowledge Discovery, 1(3), 193–214. https://doi.org/10.1002/widm.25
- Silalahi, D. F., Blakers, A., Stocks, M., Lu, B., Cheng, C., & Hayes, L. (2021). Indonesia’s vast solar energy potential. Energies, 14(17). https://doi.org/10.3390/en14175424
- Sugiawan, Y., & Managi, S. (2016). The environmental Kuznets curve in Indonesia: Exploring the potential of renewable energy. Energy Policy, 98. https://doi.org/10.1016/j.enpol.2016.08.029
- Suhono, S., Sarjiya, S., & Hadi, S. P. (2019). Electricity Demand and Supply Planning Analysis for Sumatera Interconnection System using Integrated Resources Planning Approach. Jurnal Ilmiah Teknik Elektro Komputer Dan Informatika, 5(1). https://doi.org/10.26555/jiteki.v5i1.13178
- Tan, J., Xie, X., Zuo, J., Xing, X., Liu, B., Xia, Q., & Zhang, Y. (2021). Coupling random forest and inverse distance weighting to generate climate surfaces of precipitation and temperature with Multiple-Covariates. Journal of Hydrology, 598. https://doi.org/10.1016/j.jhydrol.2021.126270
- Varatharajan, R., Manogaran, G., Priyan, M. K., Balaş, V. E., & Barna, C. (2018). Visual analysis of geospatial habitat suitability model based on inverse distance weighting with paired comparison analysis. Multimedia Tools and Applications, 77(14). https://doi.org/10.1007/s11042-017-4768-9
- Watson, D. F., & Philip, G. M. (1985). A Refinement of Inverse Distance Weighted Interpolation. Geoprocessing, 2(4), 315–327.
- Wood, S. N. (2001). mgcv: GAMs and generalized ridge regression for R. R News, 1.
- Wood, S. N. (2011). Fast stable restricted maximum likelihood and marginal likelihood estimation of semiparametric generalized linear models. Journal of the Royal Statistical Society. Series B: Statistical Methodology, 73(1), 3–36. https://doi.org/10.1111/j.1467-9868.2010.00749.x
- Wood, S. N. (2020). Mixed GAM Computation Vehicle with Automatic Smoothness Estimation. Generalized Additive Models: An Introduction with R, Second Edition, 1.8-33.
- Zhang, P. G. (2003). Time series forecasting using a hybrid ARIMA and neural network model. Neurocomputing, 50. https://doi.org/10.1016/S0925-2312(01)00702-
References
Ahmar, A. S. (2018). A comparison of α-Sutte Indicator and ARIMA methods in renewable energy forecasting in Indonesia. International Journal of Engineering and Technology(UAE), 7. https://doi.org/10.14419/ijet.v7i1.6.12319
Akritidis, D., Pozzer, A., Flemming, J., Inness, A., & Zanis, P. (2021). A Global Climatology of Tropopause Folds in CAMS and MERRA-2 Reanalyses. Journal of Geophysical Research: Atmospheres, 126(8). https://doi.org/10.1029/2020JD034115
Anggraini, M., & Indah, S. N. (2021). IESR Efforts to Accelerate Indonesia Renewable Energy Transition Through Media Relations. RSF Conference Series: Business, Management and Social Sciences, 1(4). https://doi.org/10.31098/bmss.v1i4.314
Barsi, Z. K., L�szl�, I., Szab�, G., & Abdulmutalib, H. M. (2018). Accuracy dimensions in remote sensing, International Archives of the Photogrammetry, Remote Sensing and Spatial Information Sciences - ISPRS Archives. 42(3), 61–67.
Bemporad, A. (2020). Global optimization via inverse distance weighting and radial basis functions. Computational Optimization and Applications, 77(2). https://doi.org/10.1007/s10589-020-00215-w
Bera, D., Chatterjee, N. das, & Bera, S. (2021). 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. 22, 100502.
Chang, C. H., Tan, S., Lengerich, B., Goldenberg, A., & Caruana, R. (2021). How Interpretable and Trustworthy are GAMs? Proceedings of the ACM SIGKDD International Conference on Knowledge Discovery and Data Mining. https://doi.org/10.1145/3447548.3467453
Chodakowska, E., Nazarko, J., & Nazarko, Ł. (2021). Arima models in electrical load forecasting and their robustness to noise. Energies, 14(23). https://doi.org/10.3390/en14237952
Fattah, J., Ezzine, L., Aman, Z., el Moussami, H., & Lachhab, A. (2018). Forecasting of demand using ARIMA model. International Journal of Engineering Business Management, 10. https://doi.org/10.1177/1847979018808673
Hartono, D., Hastuti, S. H., Halimatussadiah, A., Saraswati, A., Mita, A. F., & Indriani, V. (2020). Comparing the impacts of fossil and renewable energy investments in Indonesia: A simple general equilibrium analysis. Heliyon, 6(6). https://doi.org/10.1016/j.heliyon.2020.e04120
Hidayatno, A., Dhamayanti, R., & Destyanto, A. R. (2019). Model conceptualization for policy analysis in renewable energy development in Indonesia by using system dynamics. International Journal of Smart Grid and Clean Energy, 8(1). https://doi.org/10.12720/sgce.8.1.54-58
Irsyad, M. I. al, Halog, A., Nepal, R., & Koesrindartoto, D. P. (2019). The Impacts of Emission Reduction Targets in Indonesia Electricity Systems. Indonesian Journal of Energy, 2(2). https://doi.org/10.33116/ije.v2i2.42
Kuswanto, H., & Naufal, A. (2019). Evaluation of performance of drought prediction in Indonesia based on TRMM and MERRA-2 using machine learning methods. MethodsX, 6. https://doi.org/10.1016/j.mex.2019.05.029
Langer, J., Quist, J., & Blok, K. (2021). Review of renewable energy potentials in indonesia and their contribution to a 100% renewable electricity system. In Energies (Vol. 14, Issue 21). https://doi.org/10.3390/en14217033
Li, Z., Zhang, X., Zhu, R., Zhang, Z., & Weng, Z. (2020). Integrating data-to-data correlation into inverse distance weighting. Computational Geosciences, 24(1). https://doi.org/10.1007/s10596-019-09913-9
Lu, G. Y., & Wong, D. W. (2008). An adaptive inverse-distance weighting spatial interpolation technique. Computers and Geosciences, 34(9). https://doi.org/10.1016/j.cageo.2007.07.010
Musashi, J. P., Pramoedyo, H., & Fitriani, R. (2018). Comparison of Inverse Distance Weighted and Natural Neighbor Interpolation Method at Air Temperature Data in Malang Region. CAUCHY – JURNAL MATEMATIKA MURNI DAN APLIKASI, 5(2), 48–54.
Pedersen, E. J., Miller, D. L., Simpson, G. L., & Ross, N. (2019). Hierarchical generalized additive models in ecology: An introduction with mgcv. PeerJ, 2019(5). https://doi.org/10.7717/peerj.6876
Shekhar, S., Evans, M. R., Kang, J. M., & Mohan, P. (2011). Identifying patterns in spatial information: A survey of methods. Wiley Interdisciplinary Reviews: Data Mining and Knowledge Discovery, 1(3), 193–214. https://doi.org/10.1002/widm.25
Silalahi, D. F., Blakers, A., Stocks, M., Lu, B., Cheng, C., & Hayes, L. (2021). Indonesia’s vast solar energy potential. Energies, 14(17). https://doi.org/10.3390/en14175424
Sugiawan, Y., & Managi, S. (2016). The environmental Kuznets curve in Indonesia: Exploring the potential of renewable energy. Energy Policy, 98. https://doi.org/10.1016/j.enpol.2016.08.029
Suhono, S., Sarjiya, S., & Hadi, S. P. (2019). Electricity Demand and Supply Planning Analysis for Sumatera Interconnection System using Integrated Resources Planning Approach. Jurnal Ilmiah Teknik Elektro Komputer Dan Informatika, 5(1). https://doi.org/10.26555/jiteki.v5i1.13178
Tan, J., Xie, X., Zuo, J., Xing, X., Liu, B., Xia, Q., & Zhang, Y. (2021). Coupling random forest and inverse distance weighting to generate climate surfaces of precipitation and temperature with Multiple-Covariates. Journal of Hydrology, 598. https://doi.org/10.1016/j.jhydrol.2021.126270
Varatharajan, R., Manogaran, G., Priyan, M. K., Balaş, V. E., & Barna, C. (2018). Visual analysis of geospatial habitat suitability model based on inverse distance weighting with paired comparison analysis. Multimedia Tools and Applications, 77(14). https://doi.org/10.1007/s11042-017-4768-9
Watson, D. F., & Philip, G. M. (1985). A Refinement of Inverse Distance Weighted Interpolation. Geoprocessing, 2(4), 315–327.
Wood, S. N. (2001). mgcv: GAMs and generalized ridge regression for R. R News, 1.
Wood, S. N. (2011). Fast stable restricted maximum likelihood and marginal likelihood estimation of semiparametric generalized linear models. Journal of the Royal Statistical Society. Series B: Statistical Methodology, 73(1), 3–36. https://doi.org/10.1111/j.1467-9868.2010.00749.x
Wood, S. N. (2020). Mixed GAM Computation Vehicle with Automatic Smoothness Estimation. Generalized Additive Models: An Introduction with R, Second Edition, 1.8-33.
Zhang, P. G. (2003). Time series forecasting using a hybrid ARIMA and neural network model. Neurocomputing, 50. https://doi.org/10.1016/S0925-2312(01)00702-