首页|Reports on Machine Learning Findings from Polytechnic University Milan Provide N ew Insights (Just-in-Time Morning Ramp-Up Implementation in Warehouses Enabled b y Machine Learning-Based Predictive Modelling: Estimation of Achievable Energy . ..)
Reports on Machine Learning Findings from Polytechnic University Milan Provide N ew Insights (Just-in-Time Morning Ramp-Up Implementation in Warehouses Enabled b y Machine Learning-Based Predictive Modelling: Estimation of Achievable Energy . ..)
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By a News Reporter-Staff News Editor at Robotics & Machine Learning Daily News Daily News – Fresh data on artificial intelligence are presented in a new report. According to news originating from Milan, Italy, by NewsRx correspondents, research stated, “This study proposes a simulation-bas ed methodology for estimating the energy saving achievable through the implement ation of a just-in-time morning ramp-up procedure in a warehouse (equipped with a heat pump).” Funders for this research include European Union Nextgenerationeu. Our news editors obtained a quote from the research from Polytechnic University Milan: “In this methodology, the operation of the heating supply unit each day i s initiated at a different time, aiming at achieving the desired setpoint upon ( and not before) the expected arrival of the occupants. It requires the estimatio n of the ramp-up duration (the time it takes the heating system to bring the ind oor temperature to the desired setpoint), which can be provided by machine learn ing-based models. To justify the corresponding required deployment investment, a n accurate estimation of the resulting achievable energy saving is needed. Accor dingly, physics-based energy behavior simulations are first performed. Next, var ious ML algorithms are employed to estimate the ramp-up duration using the simul ated time-series data of indoor temperature, setpoints, and weather conditions.”
Polytechnic University MilanMilanIta lyEuropeCyborgsEmerging TechnologiesMachine Learning
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