Optimizing Energy Consumption in Smart Cities with Reinforcement Learning-Based Predictive Analytics

• Author(s): Ricardo Chavez
• Paper ID: 1707127
• Page: 151-164
• Published Date: 31-08-2021
• Published In: Iconic Research And Engineering Journals
• Publisher: IRE Journals
• e-ISSN: 2456-8880
• Volume/Issue: Volume 5 Issue 2 August-2021

Abstract

The fusion of fast-moving urbanization and the digital transition of cities demands the unleashing of intelligent energy management where efficiency meets sustainability and lower carbon footprints while providing high-quality life standards to city dwellers. Traditional solutions usually find it almost impractical to deal with the intensity and variability that come with urban consumption for energy given the context of inherence towards energy sources, varying demand, and changing infrastructure requirements. As a proposed solution to the challenge, deep learning- Based Predictive Analytics (RLPA) was developed to address the issue of optimizing energy for modern cities. Reinforcement learning (RL), a branch of machine learning, is used to enable the autonomously optimizing AI agents learn strategies in their environment by interactions in sequential decision-making. When coupled with predictive analytics, such systems can assist in real-time energy forecasting, the assignment of energy sources, and grid stability for a more adaptive and cost-effective energy system. This paper examines the transformative effect of RL-based predictive analytics toward minimizing energy consumption in a smart city, with a focus on enhancing demand-side energy management, ultimately promoting the reliable integration of renewable energy within the distributed grid and increasing grid resilience. A detailed survey lays down the typical models of reinforcement learning, such as Q-learning, Deep Q Networks (DQN), and Actor-Critic algorithms, to evaluate their actual usefulness in addressing energy optimization challenges at large scale. Furthermore, the incorporation of RL implementation in the smart city infrastructure, adjusting the smart grid, IoT-driven energy management systems, and demand response programs is dealt with in the research. Methodology proposed by this paper entails a comparison of the use of reinforcement learning in actual implementation of smart cities projects for efficiency in the fields of energy savings, load balancing, and operational efficiency. The result of the study initiated the unique ability it showcased for real-life smart grid application, which can change their learning mechanisms according to real-time conditions. That is, a learned ability in reinforcement learning with prediction analysis(RLPA) to respond to real-time scenario changes under renewable resources like distributed energy resources (DER), new combinations of consumer behaviors, and energy price efficiency. The current nature of this model necessitates a little motivation, which will drive it further. The results further reinforce the importance of multi-agent reinforcement learning (MARL) in decentralization energy coordination, in which a chain of AI agents together can utilize the entire city matches, optimizing for energy distribution. Although predictive analytics based on RL have an unparalleled blueprint, there are numerous challenges faced, including demands for high computations, privacy issues concerning data, and that extensive data is required for training. Issues raised in the discussed manner may finally lead to some probable future directions to be considered, such as federated learning models. Alongside these and more, they could possibly situate ideas for hybrid AI models that operate under supervised learning in conjunction with RL. Jointly, policy-based interventions are necessary to ensure ethics as these precaution-friendly assists to scale up, if not ultimately be accepted. This paper contributes to the budding body of pieces of literature that capture the driving force for AI in energy optimization by offering a comprehensive framework for integrating reinforcement learning-based predictive analytics with the smart city energy system. With responsibilities, it sets up future research directions, which include the needs for interpretability of RL models and real- time adaptability of robustness in large-scale urban settings and the strengthened alignment of cyber-attacks around our precious energy infrastructure.

Keywords

Smart cities, energy optimization, reinforcement learning, predictive analytics, demand-side management, renewable energy sources, grid resilience, AI in energy systems.

Citations

IRE Journals:
Ricardo Chavez "Optimizing Energy Consumption in Smart Cities with Reinforcement Learning-Based Predictive Analytics" Iconic Research And Engineering Journals Volume 5 Issue 2 2021 Page 151-164

IEEE:
Ricardo Chavez "Optimizing Energy Consumption in Smart Cities with Reinforcement Learning-Based Predictive Analytics" Iconic Research And Engineering Journals, 5(2)