Demand Response (DR) programs represent an effective way to optimally manage building energy demand while increasing Renewable Energy Sources (RES) integration and grid reliability, helping the ...decarbonization of the electricity sector. To fully exploit such opportunities, buildings are required to become sources of energy flexibility, adapting their energy demand to meet specific grid requirements. However, in most cases, the energy flexibility of a single building is typically too small to be exploited in the flexibility market, highlighting the necessity to perform analysis at a multiple-building scale. This study explores the economic benefits associated with the implementation of a Reinforcement Learning (RL) control strategy for the participation in an incentive-based demand response program of a cluster of commercial buildings. To this purpose, optimized Rule-Based Control (RBC) strategies are compared with a RL controller. Moreover, a hybrid control strategy exploiting both RBC and RL is proposed. Results show that the RL algorithm outperforms the RBC in reducing the total energy cost, but it is less effective in fulfilling DR requirements. The hybrid controller achieves a reduction in energy consumption and energy costs by respectively 7% and 4% compared to a manually optimized RBC, while fulfilling DR constraints during incentive-based events.
•LSTM models and DRL provide an effective data-driven district energy management.•The proposed approach reduces computational cost compared to a forward modelling.•The coordinated management achieves ...23% of peak reduction compared to baseline RBC.•The DRL controller is capable to optimize comfort, cost and peaks at district level.
Demand side management at district scale plays a crucial role in the energy transition process, being an ideal candidate to balance the needs of both users and grid, by managing the volatility of renewable sources and increasing energy flexibility. The presented study aims to explore the benefits of a coordinated approach for the energy management of a cluster of buildings to optimise the electrical demand profiles and provide services to the grid without penalising indoor comfort conditions. The proposed methodology makes use of a fully data-driven control scheme which exploits Long Short-Term Memory (LSTM) Neural Networks, and Deep Reinforcement Learning (DRL). A simulation environment is introduced to train a DRL controller to manage the operation of heat pumps and chilled and domestic hot water storage for a cluster of four buildings. LSTM models are trained with synthetic data set created in EnergyPlus and are integrated into simulation environment to evaluate the indoor temperature dynamics in each building. The developed DRL controller is tested against a manually optimised Rule Based Controller (RBC). Results show that the DRL algorithm is able to reduce the overall cluster electricity costs, while decreasing the peak energy demand by 23% and the Peak to Average Ratio (PAR) by 20%, without penalizing indoor temperature control.
Women with newly diagnosed breast cancer may have lesions undetected by conventional imaging. Recently contrast-enhanced magnetic resonance mammography (CE-MRM) showed higher sensitivity in breast ...lesions detection. The present analysis was aimed at evaluating the benefit of preoperative CE-MRM in the surgical planning. From 2005 to 2009, 525 consecutive women (25–75 years) with breast cancer, newly diagnosed by mammography, ultrasound, and needle-biopsy, underwent CE-MRM. The median invasive tumour size was 19 mm. In 144 patients, CE-MRM identified additional lesions. After secondlook, 119 patients underwent additional biopsy. CE-MRM altered surgery in 118 patients: 57 received double lumpectomy or wider excision (41 beneficial), 41 required mastectomy (40 beneficial), and 20 underwent contra lateral surgery (18 beneficial). The overall false-positive rate was 27.1% (39/144). CE-MRM contributed significantly to the management of breast cancer, suggesting more extensive disease in 144/525 (27.4%) patients and changing the surgical plan in 118/525 (22.5%) patients (99/525, 18.8% beneficial).
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