Monthly Clean Electricity Generation Forecasting in Thailand via STL, Holt, and SVR-GA
References
- A, S., Christo, M. S., Elizabeth, J., V., 2025. A hybrid approach to time series forecasting: Integrating ARIMA and prophet for improved accuracy, Results in Engineering, 27, 105703. DOI: 10.1016/j.rineng.2025.105703
- Ahmad, J., Ahmad, I., Miao, Q., Su, Z., 2025. Forecasting the human cost of disasters under Sustainable Development Goal: A time series analysis using Facebook Prophet model, Technology in Society, 83, 102992. DOI: 10.1016/j.techsoc.2025.102992
- Almazrouee, A. I., Almeshal, A. M., Almutairi, A. S., Alenezi, M. R., Alhajeri, S. N., Alshammari, F. M., 2020. Forecasting of electrical generation using prophet and multiple seasonality of holt–winters models: A case study of Kuwait, Applied Sciences, 10(23), 8412. DOI: 10.3390/app10238412
- Álvarez-Alvarado, J. M., Ríos-Moreno, J. G., Obregón-Biosca, S. A., Ronquillo-Lomelí, G., Ventura-Ramos Jr, E., Trejo-Perea, M., 2021. Hybrid techniques to predict solar radiation using support vector machine and search optimization algorithms: a review, Applied sciences, 11(3), 1044. DOI: 10.3390/app11031044
- Amin, N., Kim, Y. W., Kang, C., Byun, Y. C., 2026. Electricity load forecasting using hybrid prophet–TFT model incorporating weather data, Energy Reports, 15, 108846. DOI: 10.1016/j.egyr.2025.11.105
- Çağlayan-Akay, E., Topal, K. H., 2024. Forecasting Turkish electricity consumption: A critical analysis of single and hybrid models, Energy, 305, 132115. DOI: 10.1016/j.energy.2024.132115
- Cao, Y., Yu, J., Zhong, R., Munetomo, M., 2025. Forecasting Renewable energy and electricity consumption using evolutionary hyperheuristic algorithm, Scientific Reports, 15(1), 2565. DOI: 10.1038/s41598-025-87013-8
- Cleveland, R. B., Cleveland, W. S., McRae, J. E., Terpenning, I., 1990. STL: A seasonal-trend decomposition, J. off. Stat, 6(1), 3-73. Cortes, C., Vapnik, V., 1995. Support-vector networks. Machine learning, 20, 273-297.
- Cunha, J. L., Pereira, C. M., 2024. A hybrid model based on STL with simple exponential smoothing and ARMA for wind forecast in a Brazilian nuclear power plant site, Nuclear Engineering and Design, 421, 113026. DOI: 10.1016/j.nucengdes.2024.113026
- Dai, Y., Zhao, P., 2020. A hybrid load forecasting model based on support vector machine with intelligent methods for feature selection and parameter optimization, Applied energy, 279, 115332. DOI: 10.1016/j.apenergy.2020.115332
- Eddaoudi, Z., Aarab, Z., Boudmen, K., Elghazi, A., Rahmani, M. D., 2024. A Brief Review of Energy Consumption Forecasting Using Machine Learning Models, Procedia Computer Science, 236, 33-40. DOI: 10.1016/j.procs.2024.05.001
- Galdelli, A., Fronzi, D., Narang, G., Mancini, A., Tazioli, A., 2025. Groundwater level forecasting using data-driven models and va-dose zone: A comparative analysis of ARIMA, SARIMAX, Prophet, and NeuralProphet, Applied Computing and Geosciences, 28, 100304. DOI: 10.1016/j.acags.2025.100304
- Gong, J., Qu, Z., Zhu, Z., Xu, H., 2025. Parallel TimesNet-BiLSTM model for ultra-short-term photovoltaic power forecasting using STL decomposition and auto-tuning, Energy, 320, 135286. DOI: 10.1016/j.energy.2025.135286
- Hasan, M. S., Tarequzzaman, M., Moznuzzaman, M., Juel, M. A. A., 2025. Prediction of energy consumption in four sectors using support vector regression optimized with genetic algorithm, Heliyon, 11(2). DOI: 10.1016/j.heliyon.2025.e41765
- He, X., Zhao, W., Gao, Z., Zhang, Q., Wang, W., 2024. A hybrid prediction interval model for short-term electric load forecast using Holt-Winters and Gate Recurrent Unit, Sustainable Energy, Grids and Networks, 38, 101343. DOI: 10.1016/j.segan.2024.101343
- Hossain, M. A., Rahman, M. M., Hasan, S. S., Mahmud, A., Bai, L., 2025. Analysis and forecasting of meteorological drought using PROPHET and SARIMA models deploying machine learning technique for southwestern region of Bangladesh, Environmental and Sustainability Indicators, 27, 100761. DOI: 10.1016/j.indic.2025.100761
- Hu, G., Xu, Z., Wang, G., Zeng, B., Liu, Y., Lei, Y., 2021. Forecasting energy consumption of long-distance oil products pipeline based on improved fruit fly optimization algorithm and support vector regression, Energy, 224, 120153. DOI: 10.1016/j.energy.2021.120153
- Hyndman, R., et al., 2025, Forecast: Forecasting functions for time series and linear models, available at: https://pkg.robjhyndman.com/forecast/
- Ighravwe, D. E., Mashao, D., 2020. Analysis of support vector regression kernels for energy storage efficiency prediction, Energy Reports, 6, 634-639. DOI: 10.1016/j.egyr.2020.11.171
- Jiang, W., Wu, X., Gong, Y., Yu, W., Zhong, X., 2020. Holt–Winters smoothing enhanced by fruit fly optimization algorithm to forecast monthly electricity consumption, Energy, 193, 116779. DOI: 10.1016/j.energy.2019.116779
- Kim, H., Park, S., Kim, S., 2023. Time-series clustering and forecasting household electricity demand using smart meter data, Energy Reports, 9, 4111-4121. DOI: 10.1016/j.egyr.2023.03.042 Lafuente-Cacho, M., Izquierdo-Monge, O., Peña-Carro, P.,
- Hernández-Jiménez, Á., Callejo, L. H., Losada, A. M. P., Lamadrid, Á. L. Z., 2025. State of the Art for Solar and Wind Energy-Forecasting Methods for Sustainable Grid Integration, Current Sustainable/Renewable Energy Reports, 12(1), 1-12. DOI: 10.1007/s40518-025-00262-z
- Liu, C., Sun, B., Zhang, C., Li, F., 2020. A hybrid prediction model for residential electricity consumption using holt-winters and extreme learning machine, Applied energy, 275, 115383. DOI: 10.1016/j.apenergy.2020.115383
- Liu, H. H., Chang, L. C., Li, C. W., Yang, C. H., 2018. Particle Swarm Optimization-Based Support Vector Regression for Tourist Arrivals Forecasting, Computational Intelligence and Neuroscience, 2018(1), 6076475. DOI: 10.1155/2018/6076475
- Luo, J., Hong, T., Gao, Z., Fang, S. C., 2023. A robust support vector regression model for electric load forecasting, International Journal of Forecasting, 39(2), 1005-1020. DOI: 10.1016/j.ijfore-cast.2022.04.001
- Ma, Z., Ye, C., Ma, W., 2019. Support vector regression for predicting building energy consumption in southern China, Energy Procedia, 158, 3433-3438. DOI: 10.1016/j.egypro.2019.01.931
- Mystakidis, A., Koukaras, P., Tsalikidis, N., Ioannidis, D., Tjortjis, C., 2024. Energy forecasting: a comprehensive review of techniques and technologies, Energies, 17(7), 1662. DOI: 10.3390/en17071662
- Ouyang, Z., Ravier, P., Jabloun, M., 2021. STL decomposition of time series can benefit forecasting done by statistical methods but not by machine learning ones, Engineering Proceedings, 5(1), 42. DOI: 10.3390/engproc2021005042
- Papadimitriou, T., Gogas, P., Stathakis, E., 2014. Forecasting energy markets using support vector machines, Energy Economics, 44, 135-142. DOI: 10.1016/j.eneco.2014.03.017
- Rumbe, G., Hamasha, M., Al Mashaqbeh, S., 2024. A comparison of Holts-Winter and Artificial Neural Network approach in forecasting: A case study for tent manufacturing industry, Results in Engineering, 21, 101899. DOI: 10.1016/j.rineng.2024.101899
- Shafiuzzaman, M., Islam, M. S., Bashar, T. M. R., Munem, M., Nahiduzzaman, M., Ahsan, M., Haider, J., 2025. Enhanced very short-term load forecasting with multi-lag feature engineering and prophet-XGBoost-CatBoost architecture, Energy, 335, 137981. DOI: 10.1016/j.energy.2025.137981
- Sujjaviriyasup, T., 2025. Prediction of container throughput at Thailand’s major ports with error correction and parameter selection techniques emphasizes optimal smoothing period, Maritime Business Review, 10(2), 166-186. DOI: 10.1108/MABR-09-2024-0068
- Taylor, S. J., Letham, B., 2018. Forecasting at Scale, The American Statistician, 72(1), 37–45. DOI: 10.1080/00031305.2017.1380080
- Tebong, N. K., Simo, T., Takougang, A. N., Ntanguen, P. H., 2023. STL-decomposition ensemble deep learning models for daily reservoir inflow forecast for hydroelectricity production, Heliyon, 9(6). DOI: 10.1016/j.heliyon.2023.e16456
- Teixeira, R., Cerveira, A., Pires, E. J. S., Baptista, J., 2024. Advancing renewable energy forecasting: A comprehensive review of renewable energy forecasting methods, Energies, 17(14), 3480. DOI: 10.3390/en17143480
- Toharudin, T., Pontoh, R. S., Caraka, R. E., Zahroh, S., Lee, Y., Chen, R. C., 2021. Employing long short-term memory and Facebook prophet model in air temperature forecasting, Communications in Statistics - Simulation and Computation, 1-12. DOI: 10.1080/03610918.2020.1854302
- Tratar, L. F., Strmčnik, E., 2016. The comparison of Holt–Winters method and Multiple regression method: A case study, Energy, 109, 266-276. DOI: 10.1016/j.energy.2016.04.115
- Trull, O., García-Díaz, J. C., Troncoso, A., 2021. One-day-ahead electricity demand forecasting in holidays using discrete-interval moving seasonalities, Energy, 231, 120966. DOI: 10.1016/j.energy.2021.120966
- Waheed, W., Qingshan, X., 2024. An efficient load forecasting technique by using Holt-Winters and Prophet algorithms to mitigate the impact on power consumption in COVID-19, IET Energy Systems Integration, 6(4), 364-374. DOI: 10.1049/esi2.12132
- Wahidna, A., Sookia, N., Ramgolam, Y. K., 2024. Performance evaluation of artificial neural network and hybrid artificial neural network based genetic algorithm models for global horizontal irradiance forecasting, Solar Energy Advances, 4, 100054. DOI: 10.1016/j.seja.2024.100054
- Yang, T., Huang, L., Fu, P., Chen, X., Zhang, X., He, S., 2025. Combined multi-component composite time series power prediction model for distributed energy systems based on STL data decomposition, Measurement, 253, 117299. DOI: 10.1016/j.measurement.2025.117299
- Yang, Z., Yang, B., Liu, W., Li, M., Wang, J., Jiang, L., ..., Pan, Z., 2025. Short-Term Wind Power Forecast Based on STL-IAOA-iTransformer Algorithm: A Case Study in Northwest China, Energy Engineering, 122(2). DOI: 10.32604/ee.2025.059515
- Yin, H., Jin, D., Hong, H., Moon, J., Gu, Y. H., 2025. IAQ-STL-ML: A novel indoor air quality prediction pipeline using meta-learning framework with STL decomposition, Environmental Technology & Innovation, 38, 104107. DOI: 10.1016/j.eti.2025.104107 Zhang, B., Song, C., Jiang, X., Li, Y., 2023. Electricity price forecast based on the STL-TCN-NBEATS model, Heliyon, 9(1). DOI: 10.1016/j.heliyon.2023.e13029
- Zhou, W., Tao, H., Jiang, H., 2022. Application of a novel optimized fractional grey holt-winters model in energy forecasting, Sustainability, 14(5), 3118. DOI: 10.3390/su14053118
- Zhu, X., Song, Z., Sen, G., Tian, M., Zheng, Y., Zhu, B., 2022. Prediction study of electric energy production in important power production base, China, Scientific Reports, 12(1), 21472. DOI: 10.1038/s41598-022-25885-w
- Zhu, Y., Zhao, Y., Zhang, J., Geng, N., Huang, D., 2019. Spring onion seed demand forecasting using a hybrid Holt-Winters and support vector machine model, PloS one, 14(7), e0219889. DOI: 10.1371/journal.pone.0219889
Language: English
Page range: 581 - 595
Submitted on: Nov 27, 2025
Accepted on: Jul 23, 2026
Published online: Oct 25, 2026
Published in print: Dec 1, 2026
Published by: Quality and Production Managers Association
In partnership with: Paradigm Publishing Services
Keywords:
Related subjects:
© 2026 Thoranin Sujjaviriyasup, Mongkon Ittiphalin, published by Quality and Production Managers Association
This work is licensed under the Creative Commons Attribution 4.0 License.