Short-Term Electricity Demand Forecasting Based on Cloudy and Clear Sky Solar Irradiance Data
Received: 2 May 2025 | Revised: 17 June 2025 | Accepted: 28 June 2025 | Online: 10 July 2025
Corresponding author: Chakkrit Termritthikun
Abstract
Short-term electricity demand forecasting is a critical task for real-time decision-making. This research investigates the impact of solar irradiance components on electricity demand forecasting. The study utilized two years of hourly electricity data in addition to three key solar irradiance components: Global Horizontal Irradiance (GHI), Direct Normal Irradiance (DNI), and Diffuse Horizontal Irradiance (DHI), as clear sky indices and exogenous features. Extensive feature selection was performed to discover the best feature that enhances the forecasting accuracy of the model. Temporal Fusion Transformer (TFT) was employed as the primary model to study the influence of these exogenous variables on the electricity demand forecasting. The results demonstrated that use of solar irradiance data in the electricity demand forecasting improves the forecasting accuracy of the model. Particularly the combination of DHI and DNI clear sky indices produced the most accurate predictions. However, the addition of GHI introduced redundancy and reduced performance. The performance of the TFT model was also compared with various benchmark models. TFT outperformed the other compared models in various forecast horizons.
Keywords:
electricity demand forecasting, solar irradiance, clear sky index, deep learning, prosumer buildingDownloads
References
N. Mounir, H. Ouadi, and I. Jrhilifa, "Short-term electric load forecasting using an EMD-BI-LSTM approach for smart grid energy management system," Energy and Buildings, vol. 288, Jun. 2023, Art. no. 113022. DOI: https://doi.org/10.1016/j.enbuild.2023.113022
M. G. Pinheiro, S. C. Madeira, and A. P. Francisco, "Short-term electricity load forecasting—A systematic approach from system level to secondary substations," Applied Energy, vol. 332, Feb. 2023. DOI: https://doi.org/10.1016/j.apenergy.2022.120493
Z. Ni, C. Zhang, M. Karlsson, and S. Gong, "A study of deep learning-based multi-horizon building energy forecasting," Energy and Buildings, vol. 303, Jan. 2024, Art. no. 113810. DOI: https://doi.org/10.1016/j.enbuild.2023.113810
A.-R. Kim, D. Park, C.-S. Lee, and H. Chang, "A Comparative Analysis of Time Series Forecasting Methods for Short-Term Electricity Demand Prediction," in Proccedings of 14th International Conference on Information and Communication Technology Convergence, Jeju Island, South Korea, Jul. 2023, pp. 1235–1238. DOI: https://doi.org/10.1109/ICTC58733.2023.10392314
L. Yue, R. Zhang, J. Ding, and Q. Liu, "Real-Time Statistical Weather Estimation and Prediction for Tropical Cyclone Intensity in an Interpretable Manner via Causal Inference," IEEE Transactions on Geoscience and Remote Sensing, vol. 62, pp. 1–11, Aug. 2024. DOI: https://doi.org/10.1109/TGRS.2024.3451725
N. Klungsida, P. Maneechot, N. Butploy, and K. Khiewwan, "Forecasting Energy Consumption from EV Station Charging Using RNN, LSTM and GRU Neural Network," Journal of Renewable Energy and Smart Grid Technology, vol. 19, no. 1, pp. 1–6, Apr. 2024. DOI: https://doi.org/10.69650/rast.2024.254636
B. Lim and S. Zohren, "Time-series forecasting with deep learning: a survey," Philosophical Transactions of the Royal Society A: Mathematical, Physical and Engineering Sciences, vol. 379, no. 2194, Feb. 2021, Art. no. 20200209. DOI: https://doi.org/10.1098/rsta.2020.0209
N. E. Benti, M. D. Chaka, and A. G. Semie, "Forecasting Renewable Energy Generation with Machine Learning and Deep Learning: Current Advances and Future Prospects," Sustainability, vol. 15, no. 9, Apr. 2023, Art. no. 7087. DOI: https://doi.org/10.3390/su15097087
S. Chen, Y. Ren, D. Friedrich, Z. Yu, and J. Yu, "Prediction of office building electricity demand using artificial neural network by splitting the time horizon for different occupancy rates," Energy and AI, vol. 5, Sep. 2021, Art. no. 100093. DOI: https://doi.org/10.1016/j.egyai.2021.100093
S. Jittanon, Y. Mensin, N. Ketjoy, and C. Termritthikun, "Net Metering Prediction in Prosumer Building With Temporal Fusion Transformer Models," IEEE, vol. 12, pp. 109457–109469, Aug. 2024. DOI: https://doi.org/10.1109/ACCESS.2024.3438945
Z. Mustaffa and M. H. Sulaiman, "A Hybrid Prediction Model for Short-Term Load Forecasting in Power Systems," ECTI Transactions on Computer and Information Technology, vol. 18, no. 4, pp. 568–578, Oct. 2024. DOI: https://doi.org/10.37936/ecti-cit.2024184.257667
N. Tata, S. S. Machiraju, V. Akshay, D. M. Menon, N. B. Sai Shibu, and D. Arjun, "Prediction of Energy Consumption Using Statistical and Machine Learning Methods and Analyzing the Significance of Climate and Holidays in the Demand Prediction," in Advances in Computing and Network Communications, Singapore, Singapore, Jun. 2021, pp. 117–126. DOI: https://doi.org/10.1007/978-981-33-6987-0_10
S. A. Haider, M. Sajid, H. Sajid, E. Uddin, and Y. Ayaz, "Deep learning and statistical methods for short- and long-term solar irradiance forecasting for Islamabad," Renewable Energy, vol. 198, pp. 51–60, Oct. 2022. DOI: https://doi.org/10.1016/j.renene.2022.07.136
N. El-Amarty, M. Marzouq, H. El Fadili, S. D. Bennani, and A. Ruano, "A comprehensive review of solar irradiation estimation and forecasting using artificial neural networks: data, models and trends," Environmental Science and Pollution Research, vol. 30, no. 3, pp. 5407–5439, Nov. 2022. DOI: https://doi.org/10.1007/s11356-022-24240-w
D. S. Kumar, G. M. Yagli, M. Kashyap, and D. Srinivasan, "Solar irradiance resource and forecasting: a comprehensive review," IET Renewable Power Generation, vol. 14, no. 10, pp. 1641–1656, Jul. 2020, https://doi.org/10.1049/iet-rpg.2019.1227. DOI: https://doi.org/10.1049/iet-rpg.2019.1227
R. Wattan, I. Masiri, S. Buntoung, C. Phoemwong, and S. Janjai, "An empirical model for estimating the monthly average daily global solar radiation from ground- and satellite-based meteorological data," Journal of Renewable Energy and Smart Grid Technology, vol. 18, no. 1, pp. 29–35, Jun. 2023.
K. Dorji, S. Jittanon, P. Thanarak, P. Mensin, and C. Termritthikun, "Electricity Load Forecasting using Hybrid Datasets with Linear Interpolation and Synthetic Data," Engineering, Technology & Applied Science Research, vol. 14, no. 6, pp. 17931–17938, Dec. 2024. DOI: https://doi.org/10.48084/etasr.8577
Downloads
How to Cite
License
Copyright (c) 2025 Karma Dorji, Sorawut Jittanon, Temsiri Prompook, Yirga Belay Muna, Chakkrit Termritthikun
This work is licensed under a Creative Commons Attribution 4.0 International License.
Authors who publish with this journal agree to the following terms:
- Authors retain the copyright and grant the journal the right of first publication with the work simultaneously licensed under a Creative Commons Attribution License that allows others to share the work with an acknowledgement of the work's authorship and initial publication in this journal.
- Authors are able to enter into separate, additional contractual arrangements for the non-exclusive distribution of the journal's published version of the work (e.g., post it to an institutional repository or publish it in a book), with an acknowledgement of its initial publication in this journal.
- Authors are permitted and encouraged to post their work online (e.g., in institutional repositories or on their website) after its publication in ETASR with an acknowledgement of its initial publication in this journal.
