Proceedings of International Conference on Applied Innovation in IT
2025/08/29, Volume 13, Issue 4, pp.375-382
Forecasting Energy Consumption Using Time Series Models: A Comparison of Naïve, ARIMA, and Auto-ARIMA Approaches
Hussein Ali Ahmed and Muntadher Khamees Abstract: Accurate forecasting of energy consumption is critical for optimizing resource management and supporting sustainable development. This paper presents a comparative analysis of forecasting models for household energy consumption, evaluating the performance of simple naïve methods (Daily, Weekly, and Year-Over-Year persistence) against established statistical models (ARIMA and Auto-ARIMA). The study utilizes a real-world household power consumption dataset, employing data preprocessing, normalization, and a standardized training-testing split. Performance is rigorously evaluated using the Root Mean Square Error (RMSE) metric. The results demonstrate a clear hierarchy in forecasting accuracy: naïve models exhibit the highest error (RMSE 543.82–638.93), followed by the manually configured ARIMA model (RMSE 160–200). The Auto-ARIMA model, which automates parameter optimization, achieves the lowest error (RMSE 100–120), confirming its superior capability in capturing complex temporal patterns. This study concludes that while naïve models offer simplicity, advanced time series models like Auto-ARIMA are essential for accurate forecasting, providing a reliable tool for informed energy planning and efficiency strategies. Future work should focus on integrating exogenous variables and exploring deep learning architectures to further enhance predictive performance.
Keywords: Energy Consumption Forecasting, ARIMA, Naïve Forecasting, Time Series Dataset.
DOI: 10.25673/122141
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References:
- J. Wang and J. Hu, “A hybrid renewable energy forecasting model based on ARIMA and neural networks,” Renewable Energy, vol. 85, pp. 463–472, 2019.
- W. W. S. Wei, Time Series Analysis: Univariate and Multivariate Methods. Pearson Education, 2019.
- J. L. Torres et al., “A review of time series forecasting methods for energy demand,” Renewable and Sustainable Energy Reviews, vol. 113, p. 109292, 2019.
- W. Wang, Y. Li, and T. Chen, “Hybrid ARIMA and artificial neural network model for energy consumption prediction: A case study in China,” Energy Reports, vol. 9, pp. 123–134, 2023.
- Y. Li and J. Zhang, “Deep learning-based residential energy consumption forecasting: Comparative analysis of CNN and RNN,” Int. J. Electrical Power & Energy Systems, vol. 140, no. 3, pp. 456–468, 2022.
- R. J. Hyndman and G. Athanasopoulos, Forecasting: Principles and Practice. OTexts, 2020.
- A. Rahman and A. Khan, “Energy forecasting using ARIMA and LSTM: A case study of household energy consumption,” Energy Reports, vol. 6, pp. 123–135, 2020.
- A. Ahmed, R. Singh, and M. Patel, “A comparative analysis of traditional and machine learning models for industrial energy consumption forecasting in India,” Energy AI, vol. 5, pp. 45–56, 2022.
- Q. Zhao, Y. Sun, and J. Huang, “LSTM-based energy consumption prediction in the transportation sector of Japan,” IEEE Access, vol. 9, pp. 98765–98777, 2021.
- H. Kim, J. Park, and S. Lee, “Handling missing data in energy forecasting using autoencoders: A study in South Korea,” Journal of Renewable and Sustainable Energy, vol. 13, no. 6, pp. 6001–6012, 2021.
- Y. Huang, L. Zhang, and H. Wang, “Hybrid ARIMA-GA models for real-time energy consumption prediction: A Singaporean case study,” Applied Energy, vol. 267, pp. 115–125, 2020.
- P. Patel and A. Shah, “Support vector machines for seasonal residential energy consumption forecasting in India,” Energy Procedia, vol. 159, pp. 498–504, 2020.
- X. Chen, Y. Lin, and Z. Wang, “Analyzing seasonal patterns in commercial energy data using GRU-based deep learning models,” Energy and Buildings, vol. 183, pp. 327–339, 2019.
- M. Al-Shehri, A. Al-Ghamdi, and H. Al-Malki, “Artificial neural networks for urban energy consumption prediction in Saudi Arabia,” Energy Sustainability and Society, vol. 9, pp. 112–120, 2019.
- R. Kabbaj, O. A. Péan, L. M. Masson, J. B. Marhic, and L. Delahoche, “Occupancy states forecasting with a hidden Markov model for incomplete data, exploiting daily periodicity,” Energy and Buildings, vol. 287, p. 112985, 2023.
- R. J. Hyndman and G. Athanasopoulos, Forecasting: Principles and Practice. OTexts, 2021.
- M. Farghali et al., “Strategies to save energy in the context of the energy crisis: A review,” Environmental Chemistry Letters, vol. 21, no. 4, pp. 2003–2039, 2023.
- W. Strielkowski et al., “Renewable energy in the sustainable development of electrical power sector: A review,” Energies, vol. 14, no. 24, p. 8240, 2021.
- M. Santamouris and K. Vasilakopoulou, “Present and future energy consumption of buildings: Challenges and opportunities towards decarbonisation,” e-Prime—Advances in Electrical Engineering, Electronics and Energy, vol. 1, p. 100002, 2021.
- M. González-Torres et al., “A review on buildings energy information: Trends, end-uses, fuels and drivers,” Energy Reports, vol. 8, pp. 626–637, 2022.
- M. N. Fekri, H. Patel, K. Grolinger, and V. Sharma, “Deep learning for load forecasting with smart meter data: Online adaptive recurrent neural network,” Applied Energy, vol. 282, p. 116177, 2021.
- A. K. Dubey et al., “Study and analysis of SARIMA and LSTM in forecasting time series data,” Sustainable Energy Technologies and Assessments, vol. 47, p. 101474, 2021.
- G. Zhang, Machine Learning and Time Series Forecasting: Theoretical and Practical Applications. Springer, 2020.
- A. Walker and S. Kwon, “Analysis on impact of shared energy storage in residential community: Individual versus shared energy storage,” Applied Energy, vol. 282, p. 116172, 2021.
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