Citation :

, 2026. "Data-driven Approaches in AI for Energy Consumption Prediction" ESP International Journal of Artificial Intelligence & Data Science [IJAIDS]  Volume 1, Issue 1: 15-29.

Abstract :

Given the rising global demand as eventually growing complexity of modern power systems, accurate energy consumption forecasting has become one of fundamental requirements some efficient way to manage energy. Many of the complex, nonlinear and dynamic mechanisms that influence energy usage patterns on increasing levels can be simply due to the weather phenomenon, human behavior or economic activities (e.g. one needs predictive rules about electricity consumption), which Old forecasting approaches based on statistical and rule-based models alone cannot adequately capture [5]. In recent years, data driven methods powered by artificial intelligence (AI) have appeared as a tool with huge potential to address these issues.

References :

[1] Ahmad, T., Chen, H., Goo, Y. & Wang, J. (2018) Data-driven approaches for building energy forecasting. Energy and Buildings, 165, pp.301–320.

[2] Wei, Y., Zhang, X., Shi, Y. et al. (2018) Data-driven energy consumption prediction review. Renewable and Sustainable Energy Reviews, 82, pp.1027–1047.

[3] Amassable, K. & El-Gharry, N. (2018) Data-driven building energy prediction. Renewable and Sustainable Energy Reviews, 81, pp.1192–1205.

[4] Yesilyurt, H. et al. (2024) Energy prediction using data-driven methods. Energy.

[5] Hu, Z. (2026) AI-based renewable energy forecasting approaches. Sustainable Energy Systems.

[6] Mathumitha, R., Rethink, P. & Minimal, K. (2024) Deep learning for energy prediction. Artificial Intelligence Review.

[7] Biwa’s, P. et al. (2024) AI-driven power optimization techniques. Arrive preprint.

[8] Mishra, A. et al. (2023) LSTM-based energy forecasting model. Arrive preprint.

[9] Amity, A. & Starker, S. (2023) EV energy prediction using ML. arrive preprint.

[10] Verdecchia, R. et al. (2022) Data-centric green AI. Arrive preprint.

[11] Vapid, V. (1995) the Nature of Statistical Learning Theory. Springer.

[12] Mitchell, T. (1997) Machine Learning. McGraw-Hill.

[13] Haskin, S. (2009) Neural Networks and Learning Machines. Pearson.

[14] Aladdin, E. (2020) Introduction to Machine Learning. MIT Press.

[15] Goldberg, D. (1988) Genetic Algorithms in Optimization. Addison-Wesley.

[16] Fayyad, U., Piatetsky-Shapiro, G. & Smyth, P. (1996) Data mining and knowledge discovery. AI Magazine.

[17] Dude, R. & Hart, P. (1973) Pattern Classification. Wiley.

[18] Gellman, A. et al. (2013) Bayesian Data Analysis. CRC Press.

[19] Barnhart, R. et al. (2008) Practical Hydro informatics. Springer.

[20] Provost, F. & Fawcett, T. (2013) Data Science for Business. O’Reilly.

[21] Koschwitz, D. et al. (2018) Heating load prediction using ML. Energy.

[22] Samarra, F. et al. (2018) Random forest for energy optimization. Applied Energy.

[23] Wang, Y. et al. (2016) neural network-based energy estimation. Journal of Power Sources.

[24] Fan, C. et al. (2017) Short-term load forecasting. Energy.

[25] Sun, Y. et al. (2019) ML for smart grid prediction.

[26] Manjra’s, D. et al. (2017) Data mining in energy systems.

[27] Columnar-Santos, A. et al. (2015) Energy efficiency forecasting.

[28] International Energy Agency (2024) AI and energy demand.

[29] Sutton, R. & Barton, A. (2018) Reinforcement Learning: An Introduction. MIT Press.

[30] Good fellow, I., Bagnio, Y. & Carville, A. (2016) Deep Learning. MIT Press.

[31] Coro, G. & Perez, G. (2009) Hybrid data-driven models.

[32] Sade, L. (1965) Fuzzy sets.

[33] Haskin, S. (2001) Adaptive filter theory.

[34] Bishop, C. (2006) Pattern Recognition and Machine Learning.

[35] Hoch Reiter, S. & Schmidhuber, J. (1997) long short-term memory.

[36] Lacuna, Y. et al. (1998) Convolutional neural networks.

[37] Aswan, A. et al. (2017) Attention is all you need.

[38] Box, G. & Jenkins, G. (1970) Time-series analysis.

[39] Brownlee, J. (2017) Machine learning for time series forecasting.

[40] Zhang, G. (2003) Neural networks in forecasting.

Keywords :

Energy Consumption Prediction, Artificial Intelligence, Machine Learning, Deep Learning, Smart Grid, Load Forecasting, Time Series Analysis, Iot-Based Energy Monitoring.