Enhancing energy efficiency through the implementation of photovoltaics in municipal and household buildings

Boychev, Borislav; Malkovska, Monica; Филипова-Петракиева, Симона

doi:10.1109/bulef48056.2019.9030753

Cited by 2 publications

(1 citation statement)

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“…Many great benefits come from putting RL algorithms into electricity systems. Unlike standard rule-based methods, RL [2] lets systems change and learn from their mistakes, which makes them better over time. These abilities are especially useful in settings that are always changing, where working conditions can change without warning.…”

Section: Introductionmentioning

confidence: 99%

Enhancing Energy Efficiency in Electrical Systems with Reinforcement Learning Algorithms

P. S. Patil

2024

jes

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Improving the energy efficiency of electricity systems is important for lowering environmental damage and promoting sustainable growth. In recent years, reinforcement learning (RL) methods have become useful for finding the best ways to use energy in many areas. The point of this study is to look into how RL algorithms can be used to make electricity systems more energy efficient. The study looks into how RL algorithms can be used to make electricity systems more efficient by lowering waste, making the best use of energy, and maximizing energy use. The study suggests a new way to use RL methods to change things like power sharing, load scheduling, and resource allocation on the fly in order to keep system performance high while using as little energy as possible. Some important parts of the study method are creating RL models that work with electricity systems and their limitations, as well as coming up with the right payment functions to help people learn how to behave in ways that use less energy. Extensive models and real-world studies on sample electrical systems are used to test how well the suggested method works. According to the study's results, using RL algorithms can lead to big changes in how efficiently energy is used, with cuts in energy use running from [insert exact number range]. The study also shows how flexible and scalable RL-based solutions are when it comes to different system setups and operating scenarios. Overall, this study adds to the growing amount of research on energy efficiency by showing how RL algorithms can be used to solve difficult problems in electrical systems. Practical plans can be made to improve energy efficiency and promote sustainability in a wide range of businesses and uses based on what this study has taught us.

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Section: Introductionmentioning

confidence: 99%