Home Energy Management Systems (HEMS) have emerged as a pivotal tool in the smart home ecosystem, aiming to enhance energy efficiency, reduce costs, and improve user comfort. By enabling intelligent control and optimization of household energy consumption, HEMS plays a significant role in bridging the gap between consumer needs and energy utility objectives. However, much of the existing literature construes consumer comfort as a mere deviation from the standard appliance settings. Such deviations are typically incorporated into optimization objectives via static weighting factors. These factors often overlook the dynamic nature of consumer behaviors and preferences. Addressing this oversight, our paper introduces a multi-mode Deep Reinforcement Learning-based HEMS (DRL-HEMS) framework, meticulously designed to optimize based on dynamic, consumer-defined preferences. Our primary goal is to augment consumer involvement in Demand Response (DR) programs by embedding dynamic multi-mode preferences tailored to individual appliances. In this study, we leverage a model-free, single-agent DRL algorithm to deliver a HEMS framework that is not only dynamic but also user-friendly. To validate its efficacy, we employed real-world data at 15-minute intervals, including metrics such as electricity price, ambient temperature, and appliances' power consumption. Our results show that the model performs exceptionally well in optimizing energy consumption within different preference modes. Furthermore, when compared to traditional algorithms based on Mixed-Integer Linear Programming (MILP), our model achieves nearly optimal performance while outperforming in computational efficiency.

This study explores the crucial interplay between aggregators and building occupants in activating flexibility through Demand Response (DR) programs, with a keen focus on achieving robust decarbonization and fortifying the resilience of the energy system amidst the uncertainties presented by Renewable Energy Sources (RES). Firstly, it introduces a methodology of optimizing aggregated flexibility provision strategies in environments with limited data, utilizing Discrete Fourier Transformation (DFT) and clustering techniques to identify building occupant's activity patterns. Secondly, the study assesses the disaggregated flexibility provision of Heating Ventilation and Air Conditioning (HVAC) systems during DR events, employing machine learning and optimization techniques for precise, device-level analysis. The first approach offers a non-intrusive pathway for aggregators to provide flexibility services in environments of a single smart meter for the whole building's consumption, while the second approach carefully considers building occupants' thermal comfort profiles, while maximizing flexibility in case of existence of dedicated smart meters to the HVAC systems. Through the application of data-driven techniques and encompassing case studies from both industrial and residential buildings, this paper not only unveils pivotal opportunities for aggregators in the balancing and emerging flexibility markets but also successfully develops end-to-end practical tools for aggregators. Furthermore, the efficacy of this tool is validated through detailed case studies, substantiating its operational capability and contributing to the evolution of a resilient and efficient energy system.

Amid the increasing interest in the deployment of Distributed Energy Resources (DERs), the Virtual Power Plant (VPP) has emerged as a pivotal tool for aggregating diverse DERs and facilitating their participation in wholesale energy markets. These VPP deployments have been fueled by the Federal Energy Regulatory Commission's Order 2222, which makes DERs and VPPs competitive across market segments. However, the diversity and decentralized nature of DERs present significant challenges to the scalable coordination of VPP assets. To address efficiency and speed bottlenecks, this paper presents a novel machine learning-assisted distributed optimization to coordinate VPP assets. Our method, named LOOP-MAC(Learning to Optimize the Optimization Process for Multi-agent Coordination), adopts a multi-agent coordination perspective where each VPP agent manages multiple DERs and utilizes neural network approximators to expedite the solution search. The LOOP-MAC method employs a gauge map to guarantee strict compliance with local constraints, effectively reducing the need for additional post-processing steps. Our results highlight the advantages of LOOP-MAC, showcasing accelerated solution times per iteration and significantly reduced convergence times. The LOOP-MAC method outperforms conventional centralized and distributed optimization methods in optimization tasks that require repetitive and sequential execution.

More compute leads to greater electricity consumption, and consequent carbon emissions. A 2019 study by researchers at the University of Massachusetts Amherst estimated that the electricity consumed during the training of a transformer, a type of deep learning algorithm, can emit more than 626,000 pounds ( 284 metric tons) of carbon dioxide--equal to more than 41 round-trip flights between New York City and Sydney, Australia. We are also facing an explosion of data storage. IDC projects that 180 zettabytes of data--or, 180 billion terabytes--will be created in 2025. The collective energy required for data storage at this scale is enormous and will be challenging to address sustainably.

I get it: On one hand, you want to be a resilient off-grid solarpunk freed from the yoke of your increasingly-unreliable power company. On the other, you'd still like to enjoy creature comforts both at home and when you're on the road. It's a problem EcoFlow understands, and has turned up to CES promising to help. The company is showing off a new Whole Home Backup Solution, which ties in to its existing Delta Pro batteries. But that's less interesting to me than the gizmos which are joining the ecosystem at today's show.

BrainBox AI announced its agreement with the Brisbane Airport Corporation Pty Limited (BAC) to expand its revolutionary artificial intelligence (AI) technology across multiple precincts of Brisbane International Airport. This follows a successful trial of BrainBox AI's technology in a select area of the Brisbane Airport property (BNE). The results yielded from the pilot were significant with a 12% decrease in HVAC (heating, ventilation, and air conditioning) system energy usage, 17% reduction in building equipment run-time, and zero comfort-related customer complaints during the six-month pilot. These impressive results bolstered BAC's confidence in BrainBox AI's core product, enabling the AI's installation in other areas of the airport. Brisbane Airport Corporation, the operator of Brisbane Airport (BNE), is committed to 2030 Sustainability Targets to address carbon, energy, water, and waste impacts. Derek Boo, Head of Asset Optimisation of Brisbane Airport Corporation, spoke about the airport's approach to innovation.

Artificial intelligence is one of the most revolutionary inventions in recent history. It is an invention that has changed our lives tremendously and allowed us to do things we never could before. While Artificial Intelligence has become more advanced, people are still coming up with new ways to create AI devices that can help make life easier for humans. This blog post will list the top 5 AI inventions which have helped change our world today! Artificial intelligence is the imitation of human cognitive processes by computers, especially computer systems, in order to simulate human intellect.

A lot of energy is consumed by buildings. In fact, the Alliance to Save Energy, a nonprofit energy efficiency advocacy group, says buildings account for about 40% of all U.S. energy consumption and a similar proportion of greenhouse gas emissions. Some estimates suggest about 45% of the energy used in commercial buildings is consumed by heating, ventilation, and air conditioning (HVAC) systems, of which, as much as 30% is often wasted. Most power companies these days have energy efficiency programs that help customers identify waste and implement energy-saving measures, but there are also non-utility providers working on solutions. Montreal, Canada–based BrainBox AI is one of them. It's using artificial intelligence (AI) to significantly reduce energy consumption in buildings.

Building operations represent a significant percentage of the total primary energy consumed in most countries due to the proliferation of Heating, Ventilation and Air-Conditioning (HVAC) installations in response to the growing demand for improved thermal comfort. Reducing the associated energy consumption while maintaining comfortable conditions in buildings are conflicting objectives and represent a typical optimization problem that requires intelligent system design. Over the last decade, different methodologies based on the Artificial Intelligence (AI) techniques have been deployed to find the sweet spot between energy use in HVAC systems and suitable indoor comfort levels to the occupants. This paper performs a comprehensive and an in-depth systematic review of AI-based techniques used for building control systems by assessing the outputs of these techniques, and their implementations in the reviewed works, as well as investigating their abilities to improve the energy-efficiency, while maintaining thermal comfort conditions. This enables a holistic view of (1) the complexities of delivering thermal comfort to users inside buildings in an energy-efficient way, and (2) the associated bibliographic material to assist researchers and experts in the field in tackling such a challenge. Among the 20 AI tools developed for both energy consumption and comfort control, functions such as identification and recognition patterns, optimization, predictive control. Based on the findings of this work, the application of AI technology in building control is a promising area of research and still an ongoing, i.e., the performance of AI-based control is not yet completely satisfactory. This is mainly due in part to the fact that these algorithms usually need a large amount of high-quality real-world data, which is lacking in the building or, more precisely, the energy sector.

To demonstrate a practical use of an ML pipeline, this example uses the sample HVAC.csv data file that comes pre-loaded on the default storage for your HDInsight cluster, either Azure Storage or Data Lake Storage. HVAC.csv contains a set of times with both target and actual temperatures for HVAC (heating, ventilation, and air conditioning) systems in various buildings. The goal is to train the model on the data, and produce a forecast temperature for a given building.