Machine-Learning-Based Prediction of HVAC-Driven Load Flexibility in Warehouses

This paper introduces a methodology for predicting a warehouse’s reduced load while offering flexibility. Physics-based energy simulations are first performed to model flexibility events, which involve adjusting cooling setpoints with controlled temperature increases to reduce the cooling load. The warehouse building encompasses office and storage spaces, and three cooling scenarios are implemented, i.e., exclusive storage area cooling, exclusive office area cooling, and cooling in both spaces, to expand the study’s potential applications. Next, the simulation data are utilized for training machine learning (ML)-based pipelines, predicting five subsequent hourly energy consumption values an hour before the setpoint adjustments, providing time to plan participation in demand response programs or prepare for charging electric vehicles. For each scenario, the performance of an Artificial Neural Network (ANN) and a tree-based ML algorithm are compared. Moreover, an expanding window scheme is utilized, gradually incorporating new data and emulating online learning. The results indicate the superior performance of the tree-based algorithm, with an average error of less than 3.5% across all cases and a maximum hourly error of 7%. The achieved accuracy confirms the method’s reliability even in dynamic scenarios where the integrated load of storage space and offices needs to be predicted

Assessing the impact of employing machine learning-based baseline load prediction pipelines with sliding-window training scheme on offered flexibility estimation for different building categories

The present study is focused on assessing the impact of the performance of baseline load prediction pipelines on the estimation (by the grid operator) accuracy of the flexibility offered by different categories of buildings. Accordingly, the corresponding impact of employing different machine learning (ML) algorithms, with sliding-window and offline training schemes, for hour-ahead baseline load prediction has been investigated and compared. Using a smart meter measurements dataset, training window sizes and the most promising pipeline for each building category are first identified. Next, the consumption profiles of five buildings (belonging to each category), with the regular operation (baseline load) and while offering flexibility, are physically simulated. Finally, the identified pipelines are used for predicting the baseline loads, and the resulting error in estimating the provided flexibility is determined. Obtained results demonstrate that the identified most promising prediction pipeline (extra trees algorithm with a sliding window of 5 weeks) offers a notably superior performance compared to that of offline training (average score of 0.91 vs. 0.87). Employing these pipelines permits estimating the provided flexibility with acceptable accuracy (flexibility index's mean relative error between -2.45% to +2.79%), permitting the grid operator to guarantee fair compensation for buildings' offered flexibility.publishedVersio

Case report: Ponatinib as a bridge to CAR-T cells and subsequent maintenance in a patient with relapsed/refractory Philadelphia-like acute lymphoblastic leukemia

Philadelphia (Ph)-like acute lymphoblastic leukemia (ALL) constitutes a heterogeneous subset of ALL with a uniformly unfavorable prognosis. The identification of mutations amenable to treatment with tyrosine kinase-inhibitors (TKIs) represents a promising field of investigation. We report the case of a young patient affected by relapsed/refractory Ph-like ALL treated with chimeric antigen receptor T (CAR-T) cells after successful bridging with compassionate-use ponatinib and low-dose prednisone. We restarted low-dose ponatinib maintenance three months later. Twenty months later, measurable residual disease negativity and B-cell aplasia persist. To the best of our knowledge, this is the first case reporting the use of ponatinib in Ph-like ALL as a bridge to and maintenance after CAR-T cell therapy

Esame anatomico della ghiandola parotide mediante risonanza magnetica nucleare

In this work are been examined the parotid gland of 30 patients. The RM is been showed very accurate about the anatomic study of the parotid gland

Valutazione radiografica dell'aspergillosi polmonare invasiva (IPA)

non disponibil

L'ecotomografia epatica nell'iter diagnostico dei pazienti affetti da epatite cronica

non disponibil