# Energy management of small-scale PV-battery systems: A systematic review considering practical implementation, computational requirements, quality of input data and battery degradation

arXiv: https://arxiv.org/abs/1905.09498 · HTML: https://mlsanity.com/paper/1905.09498/

## Key facts

- Cited by 3 papers in the map (data through 14 Oct 2026).
- First posted on arXiv 23 May 2019.

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

- [A Reinforcement Learning Approach to Dairy Farm Battery Management using Q Learning](https://mlsanity.com/paper/2403.09499/) (2024)
- [Reinforcement Learning for Battery Management in Dairy Farming](https://mlsanity.com/paper/2308.09023/) (2023)
- [An Extensive and Methodical Review of Smart Grids for Sustainable Energy Management-Addressing Challenges with AI, Renewable Energy Integration and Leading-edge Technologies](https://mlsanity.com/paper/2501.14143/) (2025)

## Abstract (arXiv)

The home energy management problem has many different facets, including economic viability, data uncertainty and quality of strategy employed. The existing literature in this area focuses on individual aspects of this problem without a detailed, holistic analysis of the results with regards to practicality in implementation. In this paper, we fill this gap by performing a comprehensive comparison of seven different energy management strategies, each with different levels of practicality, sophistication and computational requirements. We analyse the results in the context of these three characteristics, and also critique the modelling assumptions made by each strategy. Our analysis finds that using a more sophisticated energy management strategy may not necessarily improve the performance and economic viability of the PV-battery system due to the effects of modeling assumptions, such as the treatment of uncertainties in the input data and battery degradation effects.