As the electricity market is progressively liberalized, virtual bidding has emerged as a novel participation mechanism attracting increasing attention. This paper integrates evolutionary game theory ...

Utilities worldwide are turning to artificial intelligence (AI) and machine learning to stabilize networks, forecast consumption, detect faults, and optimize system performance in real-time. What was ...

AI improves renewable energy forecasting accuracy by up to 33%, helping grid operators better integrate solar and wind resources. Predictive maintenance powered by AI reduces equipment downtime by ...

Accurate land use/land cover (LULC) classification remains a persistent challenge in rapidly urbanising regions especially, in the Global South, where cloud cover, seasonal variability, and limited ...

Extreme events such as earthquakes can readily cause structural damage and operational disturbances in power grids, thereby weakening the system’s supply stability and recovery capability and posing ...

Abstract: Reinforcement learning algorithms have revolutionized autonomous decision-making in various domains. In this paper, we compare Q-learning and DQN for solving a 100x100 grid model of a ...

Abstract: To address the issues of slow convergence speed and poor path planning performance in dynamic obstacle environments. This paper proposes an improved Q-Learning path planning algorithm for ...

A machine learning algorithm used gene expression profiles of patients with gout to predict flares. The PyTorch neural network performed best, with an area under the curve of 65%. The PyTorch model ...

In this tutorial, we explore how exploration strategies shape intelligent decision-making through agent-based problem solving. We build and train three agents, Q-Learning with epsilon-greedy ...

Like humans, artificial intelligence learns by trial and error, but traditionally, it requires humans to set the ball rolling by designing the algorithms and rules that govern the learning process.