11,400 research outputs found
A Review on Application of Artificial Intelligence Techniques in Microgrids
A microgrid can be formed by the integration of different components such as loads, renewable/conventional units, and energy storage systems in a local area. Microgrids with the advantages of being flexible, environmentally friendly, and self-sufficient can improve the power system performance metrics such as resiliency and reliability. However, design and implementation of microgrids are always faced with different challenges considering the uncertainties associated with loads and renewable energy resources (RERs), sudden load variations, energy management of several energy resources, etc. Therefore, it is required to employ such rapid and accurate methods, as artificial intelligence (AI) techniques, to address these challenges and improve the MG's efficiency, stability, security, and reliability. Utilization of AI helps to develop systems as intelligent as humans to learn, decide, and solve problems. This paper presents a review on different applications of AI-based techniques in microgrids such as energy management, load and generation forecasting, protection, power electronics control, and cyber security. Different AI tasks such as regression and classification in microgrids are discussed using methods including machine learning, artificial neural networks, fuzzy logic, support vector machines, etc. The advantages, limitation, and future trends of AI applications in microgrids are discussed.©2022 IEEE. Personal use of this material is permitted. Permission from IEEE must be obtained for all other uses, in any current or future media, including reprinting/republishing this material for advertising or promotional purposes, creating new collective works, for resale or redistribution to servers or lists, or reuse of any copyrighted component of this work in other works.fi=vertaisarvioitu|en=peerReviewed
Evolving Inborn Knowledge For Fast Adaptation in Dynamic POMDP Problems
Rapid online adaptation to changing tasks is an important problem in machine
learning and, recently, a focus of meta-reinforcement learning. However,
reinforcement learning (RL) algorithms struggle in POMDP environments because
the state of the system, essential in a RL framework, is not always visible.
Additionally, hand-designed meta-RL architectures may not include suitable
computational structures for specific learning problems. The evolution of
online learning mechanisms, on the contrary, has the ability to incorporate
learning strategies into an agent that can (i) evolve memory when required and
(ii) optimize adaptation speed to specific online learning problems. In this
paper, we exploit the highly adaptive nature of neuromodulated neural networks
to evolve a controller that uses the latent space of an autoencoder in a POMDP.
The analysis of the evolved networks reveals the ability of the proposed
algorithm to acquire inborn knowledge in a variety of aspects such as the
detection of cues that reveal implicit rewards, and the ability to evolve
location neurons that help with navigation. The integration of inborn knowledge
and online plasticity enabled fast adaptation and better performance in
comparison to some non-evolutionary meta-reinforcement learning algorithms. The
algorithm proved also to succeed in the 3D gaming environment Malmo Minecraft.Comment: 9 pages. Accepted as a full paper in the Genetic and Evolutionary
Computation Conference (GECCO 2020
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