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Hybrid intelligence aims to enhance decision-making, problem-solving, and overall system performance by combining the strengths of both, human cognitive abilities and artificial intelligence. With the rise of Large Language Models (LLM),…

As more and more automatic vehicles, power consumption prediction becomes a vital issue for task scheduling and energy management. Most research focuses on automatic vehicles in transportation, but few focus on automatic ground vehicles…

This study aimed to comprehend how user domain knowledge and artificial intelligence (AI) literacy impact the effective use of human-AI interactive building energy management system (BEMS). While prior studies have investigated the…

The transition to 4th generation district heating creates a growing need for scalable, automated design tools that accurately capture the spatial and temporal details of heating network operation. This paper presents an automated design…

As artificial intelligence (AI) continues to rapidly evolve, the realm of Earth and atmospheric sciences is increasingly adopting data-driven models, powered by progressive developments in deep learning (DL). Specifically, DL techniques are…

Heat pumps are essential for decarbonizing residential heating but consume substantial electrical energy, impacting operational costs and grid demand. Many systems run inefficiently due to planning flaws, operational faults, or…

Artificial intelligence (AI) is driving unprecedented growth in data center (DC) scale and power demand. AI workloads impose highly dynamic, difficult-to-forecast power profiles on the utility grid, creating reliability and stability…

District Heating Systems are essential infrastructure for delivering heat to consumers across a geographic region sustainably, yet efficient management relies on optimizing diverse energy sources, such as wood, gas, electricity, and solar,…

AIGC images are prevalent across various fields, yet they frequently suffer from quality issues like artifacts and unnatural textures. Specialized models aim to predict defect region heatmaps but face two primary challenges: (1) lack of…

This paper presents a Deep Learning (DL) framework for 48-hour forecasting of temperature, solar irradiance, and relative humidity to support Model Predictive Control (MPC) in smart HVAC systems. The approach employs a stacked Bidirectional…

The advent of edge devices dedicated to machine learning tasks enabled the execution of AI-based applications that efficiently process and classify the data acquired by the resource-constrained devices populating the Internet of Things. The…

Classical methods to control heating systems are often marred by suboptimal performance, inability to adapt to dynamic conditions and unreasonable assumptions e.g. existence of building models. This paper presents a novel deep reinforcement…

Optimal control of thermostatically controlled loads connected to a district heating network is considered a sequential decision- making problem under uncertainty. The practicality of a direct model-based approach is compromised by two…

In recent years, artificial intelligence (AI) has deeply impacted various fields, including Earth system sciences. Here, AI improved weather forecasting, model emulation, parameter estimation, and the prediction of extreme events. However,…

The energy transition has recently experienced a further acceleration. In order to make the integration of renewable energies as cost-effective, secure and sustainable as possible and to develop new paradigms for the energy system, many…

This work presents a scalable Bayesian modeling framework for evaluating building energy performance using smart-meter data from 2,788 Danish single-family homes. The framework leverages Bayesian statistical inference integrated with Energy…

Large-scale deep learning workloads increasingly suffer from I/O bottlenecks as datasets grow beyond local storage capacities and GPU compute outpaces network and disk latencies. While recent systems optimize data-loading time, they…

Knowledge-based or Artificial Intelligence techniques are used increasingly as alternatives to more classical techniques to model ENVIRONMENTAL SYSTEMS. Use of Artificial Intelligence (AI) in environmental modelling has increased with…

The contemporary era is marked by rapid urban growth and increasing population. A significant, and constantly growing, portion of the global population now resides in major cities, leading to escalating energy demands in urban centers. As…

Fifth generation district heating and cooling (5GDHC) networks accelerate the use of renewable energies in the heating sector and enable flexible, efficient and future-proof heating and cooling supply via a single network. Due to their low…