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Search Results (3)

Search Parameters:
Keywords = Energym

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13 pages, 263 KiB  
Article
Validation of the Energy Matrix of Guanidinoacetic Acid for Broiler Chickens: Effects on Performance, Carcass Traits, and Meat Quality
by Fernanda Danieli Antoniazzi Valentini, Heloísa Pagnussatt, Fernanda Picoli, Letieri Griebler, Carine de Freitas Milarch, Arele Arlindo Calderano, Fernando de Castro Tavernari and Tiago Goulart Petrolli
Poultry 2025, 4(3), 30; https://doi.org/10.3390/poultry4030030 - 14 Jul 2025
Viewed by 269
Abstract
The objective of this study was to validate the energy matrix of guanidinoacetic acid (AGA) in broiler diets, assessing its effects on performance, carcass traits, organ development, and meat quality. The experiment was conducted at the UNOESC Xanxerê poultry facility using 480 COBB [...] Read more.
The objective of this study was to validate the energy matrix of guanidinoacetic acid (AGA) in broiler diets, assessing its effects on performance, carcass traits, organ development, and meat quality. The experiment was conducted at the UNOESC Xanxerê poultry facility using 480 COBB broilers in a completely randomized design with three treatments: positive control (standard energy level), negative control (75 kcal/kg reduction in metabolizable energy—ME), and negative control + AGA (600 mg/kg). Male broilers in the positive control and negative control + AGA groups showed improved feed conversion, higher weight gain, and greater feed intake (p < 0.001) compared to the negative control group. A significant difference in relative liver weight (p = 0.037) was observed between the positive and negative control groups. Birds supplemented with AGA had higher blood glucose levels and lower levels of cholesterol (p = 0.013), triglycerides (p = 0.005), total proteins (p < 0.001), and creatinine (p = 0.056). Regarding meat quality, the AGA-supplemented group showed higher crude protein content and greater lipid peroxidation in breast meat. In conclusion, the inclusion of AGA using an energy matrix reduced by 75 kcal/kg ME is feasible, maintaining performance and carcass characteristics while improving meat quality in broiler chickens. Full article
33 pages, 28768 KiB  
Article
Evaluating Reinforcement Learning Algorithms in Residential Energy Saving and Comfort Management
by Charalampos Rafail Lazaridis, Iakovos Michailidis, Georgios Karatzinis, Panagiotis Michailidis and Elias Kosmatopoulos
Energies 2024, 17(3), 581; https://doi.org/10.3390/en17030581 - 25 Jan 2024
Cited by 12 | Viewed by 3447
Abstract
The challenge of maintaining optimal comfort in residents while minimizing energy consumption has long been a focal point for researchers and practitioners. As technology advances, reinforcement learning (RL)—a branch of machine learning where algorithms learn by interacting with the environment—has emerged as a [...] Read more.
The challenge of maintaining optimal comfort in residents while minimizing energy consumption has long been a focal point for researchers and practitioners. As technology advances, reinforcement learning (RL)—a branch of machine learning where algorithms learn by interacting with the environment—has emerged as a prominent solution to this challenge. However, the modern literature exhibits a plethora of RL methodologies, rendering the selection of the most suitable one a significant challenge. This work focuses on evaluating various RL methodologies for saving energy while maintaining adequate comfort levels in a residential setting. Five prominent RL algorithms—Proximal Policy Optimization (PPO), Deep Deterministic Policy Gradient (DDPG), Deep Q-Network (DQN), Advantage Actor-Critic (A2C), and Soft Actor-Critic (SAC)—are being thoroughly compared towards a baseline conventional control approach, exhibiting their potential to improve energy use while ensuring a comfortable living environment. The integrated comparison between the different RL methodologies emphasizes the subtle strengths and weaknesses of each algorithm, indicating that the best selection relies heavily on particular energy and comfort objectives. Full article
(This article belongs to the Special Issue Advanced Energy Systems in Zero/Positive Energy Buildings, Communities and Districts)
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19 pages, 1577 KiB  
Article
Energym: A Building Model Library for Controller Benchmarking
by Paul Scharnhorst, Baptiste Schubnel, Carlos Fernández Bandera, Jaume Salom, Paolo Taddeo, Max Boegli, Tomasz Gorecki, Yves Stauffer, Antonis Peppas and Chrysa Politi
Appl. Sci. 2021, 11(8), 3518; https://doi.org/10.3390/app11083518 - 14 Apr 2021
Cited by 42 | Viewed by 5570
Abstract
We introduce the Python-based open-source library Energym, a building model library to test and benchmark building controllers. The incorporated building models are presented with a brief explanation of their function, location and technical equipment. Furthermore, the library structure is described, highlighting the necessary [...] Read more.
We introduce the Python-based open-source library Energym, a building model library to test and benchmark building controllers. The incorporated building models are presented with a brief explanation of their function, location and technical equipment. Furthermore, the library structure is described, highlighting the necessary features to provide the benchmarking and control capabilities, i.e., standardized evaluation scenarios, key performance indicators (KPIs) and forecasts of uncertain variables. We go on to characterize the evaluation scenarios for each of the models and give formal definitions of the KPIs. We describe the calibration methodologies used for constructing the models and illustrate their usage through examples. Full article
(This article belongs to the Special Issue Sustainable Built Environments in 21st Century)
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