# Big Data Analytics for Dynamic Energy Management in Smart Grids

arXiv: https://arxiv.org/abs/1504.02424 · HTML: https://mlsanity.com/paper/1504.02424/

## Key facts

- Cited by 3 papers in the map (data through 14 Oct 2026).
- First posted on arXiv 9 Apr 2015.

## Built on by (most cited first, 3 of 3)

- [Review of Low Voltage Load Forecasting: Methods, Applications, and Recommendations](https://mlsanity.com/paper/2106.00006/) (2021)
- [Wireless Federated Learning (WFL) for 6G Networks -- Part I: Research Challenges and Future Trends](https://mlsanity.com/paper/2105.00842/) (2021)
- [Sustainability and Artificial Intelligence: Necessary, Challenging, and Promising Intersections](https://mlsanity.com/paper/2606.09006/) (2026)

## Abstract (arXiv)

The smart electricity grid enables a two-way flow of power and data between suppliers and consumers in order to facilitate the power flow optimization in terms of economic efficiency, reliability and sustainability. This infrastructure permits the consumers and the micro-energy producers to take a more active role in the electricity market and the dynamic energy management (DEM). The most important challenge in a smart grid (SG) is how to take advantage of the users' participation in order to reduce the cost of power. However, effective DEM depends critically on load and renewable production forecasting. This calls for intelligent methods and solutions for the real-time exploitation of the large volumes of data generated by a vast amount of smart meters. Hence, robust data analytics, high performance computing, efficient data network management, and cloud computing techniques are critical towards the optimized operation of SGs. This research aims to highlight the big data issues and challenges faced by the DEM employed in SG networks. It also provides a brief description of the most commonly used data processing methods in the literature, and proposes a promising direction for future research in the field.