Blog

AI-Driven Load Forecasting for Smart Grids Using Real-Time Data Fusion

Book Title: Transformative Approaches in Multidisciplinary Research (TAMR)

Chief Editors: Dr. Meenakshi Kujur, and Dr. Hamidun Bunawan

Associate Editors: Mr. Gunjit Singhal, and Dr. Asma Farooque

Co-Editors: Dr. Suresh Kamarapu, and Dr. Souvik Sur

Chapter: 12

DOI: https://doi.org/10.59646/672/12

Authors: Dr. Shaik Hussain Vali, and Dr. Pagidela Yamuna

Abstract

The fast development of technologies related to smart grids has stimulated the adoption of artificial intelligence (AI) in the energy management system that alters the manner in which electricity demand is forecasted and balanced in a variety of consumption settings. The research paper discusses the potential of AI-based load forecasting based on real-time data fusion to enhance grid efficiency, reliability and sustainability in various energy consumption scenarios. The research is a mixed-method one, as it uses both quantitative data collected using smart meters, weather statistics, and historical load data as well as qualitative data collected in the form of a structured interview with the energy analysts and grid operators. The results indicate substantial differences in the accuracy of the forecasts and efficiency of implementation: urban smart grids are more precise because of the high-quality infrastructure, high density of sensors and effective communication systems, semi-urban and rural grids experience difficulties in terms of the sparsity of data, the number of sensors, and the constraints of the infrastructures. In spite of these constraints, AI-based models, especially the ones that combine machine learning and deep learning methods with real-time data streams, have significantly improved the accuracy of load prediction, minimized the energy loss, and provided the option to proactively manage demand on the demand side. Moreover, the feedback of the stakeholders shows a balanced viewpoint; urban operators are focused on efficiency and automation, whereas operators in less-developed regions are concerned about a better level of reliability and better outage control under the limitations of technologies. The paper also suggests the strategic frameworks and policy interventions to close the implementation gaps, such as investing in smart infrastructure, improving the data integration platforms and building the standardized regulatory policies concerning AI utilization in the energy systems. This study highlights the transformative nature of AI-based load forecasting performance by focusing on the contextual differences in its performance, showing its potential in promoting the resilience of smart grids, optimization of energy distribution, and sustainable energy transitions.

The comparative analysis can provide useful insights to policy makers, utility providers and developers of technology to make intelligent energy systems more scalable and inclusive. The results add to the existing literature on AI in energy systems by offering evidence-based advice on the successful implementation of real-time data fusion method in heterogeneous grid context.

Keywords: AI driven load forecasting, Smart grids, Real-time data fusion, Energy management, Machine learning, Deep learning, Smart meters, Grid reliability, Energy efficiency, Demand forecasting.

References

  1. Hong, T., Pinson, P., & Fan, S. (2016). Global energy forecasting competition 2012 and beyond: Introduction and overview. International Journal of Forecasting, 32(3), 896–913. [https://doi.org/10.1016/j.ijforecast.2015.09.001](https://doi.org/10.1016/j.ijforecast.2015.09.001)
  2. Weron, R. (2014). Electricity price forecasting: A review of the state-of-the-art with a look into the future. International Journal of Forecasting, 30(4), 1030–1081. [https://doi.org/10.1016/j.ijforecast.2014.08.008](https://doi.org/10.1016/j.ijforecast.2014.08.008)
  3. Suganthi, L., & Samuel, A. A. (2012). Energy models for demand forecasting—A review. Renewable and Sustainable Energy Reviews, 16(2), 1223–1240. [https://doi.org/10.1016/j.rser.2011.08.014](https://doi.org/10.1016/j.rser.2011.08.014)
  4. Hippert, H. S., Pedreira, C. E., & Souza, R. C. (2001). Neural networks for short-term load forecasting: A review and evaluation. IEEE Transactions on Power Systems, 16(1), 44–55. [https://doi.org/10.1109/59.910780](https://doi.org/10.1109/59.910780)
  5. Monteiro, C., & Jorge, H. (2020). Smart grid data analytics and forecasting using machine learning techniques. Energies, 13(10), 2542. [https://doi.org/10.3390/en13102542](https://doi.org/10.3390/en13102542)
  6. Ahmad, T., Zhang, D., Huang, C., Zhang, H., Dai, N., Song, Y., & Chen, H. (2020). Artificial intelligence in sustainable energy industry: Status Quo, challenges and opportunities. Journal of Cleaner Production, 289, 125834. [https://doi.org/10.1016/j.jclepro.2020.125834](https://doi.org/10.1016/j.jclepro.2020.125834)
  7. Kong, W., Dong, Z. Y., Jia, Y., Hill, D. J., Xu, Y., & Zhang, Y. (2017). Short-term residential load forecasting based on LSTM recurrent neural network. IEEE Transactions on Smart Grid, 10(1), 841–851. [https://doi.org/10.1109/TSG.2017.2753802](https://doi.org/10.1109/TSG.2017.2753802)
  8. Mocanu, E., Nguyen, P. H., Gibescu, M., & Kling, W. L. (2016). Deep learning for estimating building energy consumption. Sustainable Energy, Grids and Networks, 6, 91–99. [https://doi.org/10.1016/j.segan.2016.02.005](https://doi.org/10.1016/j.segan.2016.02.005)
  9. Taieb, S. B., Hyndman, R. J., & Ord, J. K. (2012). Forecasting in the presence of nonlinearity and non-normality. International Journal of Forecasting, 28(2), 468–480. [https://doi.org/10.1016/j.ijforecast.2011.08.002](https://doi.org/10.1016/j.ijforecast.2011.08.002)
  10. Liu, N., Yu, X., Wang, C., & Li, J. (2017). Energy-sharing model with price-based demand response for microgrids of peer-to-peer prosumers. IEEE Transactions on Power Systems, 32(5), 3569–3583. [https://doi.org/10.1109/TPWRS.2016.2636122](https://doi.org/10.1109/TPWRS.2016.2636122)
  11. Ghosh, S., & Nirmal, J. (2021). Machine learning-based electrical load forecasting: A review and comparative analysis. Renewable and Sustainable Energy Reviews, 151, 111631. [https://doi.org/10.1016/j.rser.2021.111631](https://doi.org/10.1016/j.rser.2021.111631)
  12. Marino, D. L., Amarasinghe, K., & Manic, M. (2016). Building energy load forecasting using deep neural networks. IEEE IECON 2016 – 42nd Annual Conference of the IEEE Industrial Electronics Society, 7046–7051. [https://doi.org/10.1109/IECON.2016.7793413](https://doi.org/10.1109/IECON.2016.7793413)
  13. Chen, Y., & Wang, Z. (2020). Machine learning approaches for smart grid energy forecasting: A survey. Energy Reports, 6, 237–252. [https://doi.org/10.1016/j.egyr.2019.11.038](https://doi.org/10.1016/j.egyr.2019.11.038)
  14. Park, S. H., Lee, S. H., & Choi, H. J. (2019). Short-term load forecasting using LSTM neural networks with real-time data fusion. Applied Energy, 250, 1471–1480. [https://doi.org/10.1016/j.apenergy.2019.05.107](https://doi.org/10.1016/j.apenergy.2019.05.107)