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The flat-plate solar collector (FPSC) three-dimensional (3D) model was used to numerically evaluate the energy and economic estimates. A laminar flow with 500 ≤ Re ≤ 1900, an inlet temperature of 293 K, and a solar flux of 1000W/m² were assumed the operating conditions. Two mono nanofluids, CuO-DW and Cu-DW, were tested with different shapes (Spherical, Cylindrical, Platelets, and Blades) and different volume fractions. Additionally, hybrid nanocomposites from CuO@Cu/DW with different shapes (Spherical, Cylindrical, Platelets and Blades), different mixing ratios (60%+40%, 50%+50% and 40%+60%) and different volume fractions (1 volume%, 2 volume%, 3 volume% and 4 volume%) were compared with mono nanofluids. At 1 volume% and Re = 1900, CuO-Platelets demonstrated the highest pressure drop (33.312 Pa). CuO-Platelets achieved the higher thermal enhancement with (8.761%) at 1 vol.% and Re = 1900. CuO-Platelets reduced the size of the solar collector by 25.60%. Meanwhile, CuO@Cu-Spherical (40:60) needed a larger collector size with 16.69% at 4 vol.% and Re = 1900. CuO-Platelets with 967.61, CuO -- Cylindrical with 976.76, Cu Platelets with 983.84, and Cu-Cylindrical with 992.92 presented the lowest total cost. Meanwhile, the total cost of CuO -- Cu -- Platelets with 60:40, 50:50, and 40:60 was 994.82, 996.18, and 997.70, respectively. [ABSTRACT FROM AUTHOR]

The electrical demand and generation in power systems is currently the biggest source of uncertainty for an electricity provider. For a dependable and financially advantageous electricity system, demand response (DR) success as a result of household appliance energy management has attracted significant attention. Due to fluctuating electricity rates and usage trends, determining the best schedule for apartment appliances can be difficult. As a result of this context, the Improved Cockroach Swarm Optimization Algorithm (ICSOA) is combined with the Innovative Apartments Appliance Scheduling (IAAS) framework. Using the proposed technique, the cost of electricity reduction, user comfort maximization, and peak-to-average ratio reduction are analyzed for apartment appliances. The proposed framework is evaluated by comparing it with BFOA and W/O scheduling cases. In comparison to the W/O scheduling case, the BFOA method lowered energy costs by 17.75%, but the ICSA approach reduced energy cost by 46.085%. According to the results, the created ICSA algorithm performed better than the BFOA and W/O scheduling situations in terms of the stated objectives and was advantageous to both utilities and consumers. [ABSTRACT FROM AUTHOR]

The bang-bang relays of the multiple-boiler system (MBS) control, are characterized by complex limiter saturation functions and classified as fixed parameters. Their action signals cannot precisely control the nonlinear dynamic building heating demand over their entire range of operation. Moreover, in a mono-boiler system, the bang-bang controller endures increasing short cycling over partial load time due to the heating system being considered to have an oversized boiler at most times of running, thus promoting high energy consumption and fluctuating indoor thermal comfort. So, it is difficult to cope with uncertainties in outdoor environments and indoor heating load. Hence, this study formulates the MBS control problem as a dynamic Markov decision process and applies a deep clustering of reinforcement learning approach to obtain the optimal control policy through interaction with the environment based on multi-agent learning according to bang-bang action. With such an approach, adopting a new boiler sequencing control (BSC) strategy using deep clustering of reinforcement learning based on a bang-bang (DCRLBB) manner. The deep clustering is configured to break Lagrangian trajectory curves into piecewise segments to represent the RL agent's action policy. The agent's action policy signals are configured from the bang-bang reward formula based on trade-off implications to be more adjustable than traditional fixed parameters such as fuzzy bang-bang controller (FBBC). The agent of BSC significantly affects the energy performance of the MBS, whereas the other agent resizes boiler capacity by acting to adjust the boiler solenoid fuel valve. The comparison of results between the proposed strategy and conventional FBBC shows distinct differences in the superior response of DCRLBB under dynamic indoor/outdoor actual conditions and energy saving by more than 32% while maintaining the indoor thermal in the comfortable range. [Display omitted] • Proposed strategy based on DCRLBB is used to minimize short cycling on oversized boilers. • DCRLBB provides a viable approach to handling various cooperative policies of multi-agent • Clustering technique helps the well-fitting of the Lagrangian model to optimal agent policy. • Online learning for active decision-making enables the agent to predict the power demand of a multi-boiler. • The comparison of results for the DCRLBB shows energy saving by more than 32%. [ABSTRACT FROM AUTHOR]

The intelligent buildings provided various incentives to get highly inefficient energy-saving caused by the non-stationary building environments. In the presence of such dynamic excitation with higher levels of nonlinearity and coupling effect of temperature and humidity, the HVAC system transitions from underdamped to overdamped indoor conditions. This led to the promotion of highly inefficient energy use and fluctuating indoor thermal comfort. To address these concerns, this study develops a novel framework based on deep clustering of lagrangian trajectories for multi-task learning (DCLTML) and adding a pre-cooling coil in the air handling unit (AHU) to alleviate a coupling issue. The proposed DCLTML exhibits great overall control and is suitable for multi-objective optimisation based on cooperative multi-agent systems (CMAS). The framework of DCLTML is used greedy iterative training to get an optimal set of weights and tabulated as a layer for each clustering structure. Such layers can deal with the challenges of large space and its massive data. Then the layer weights of each cluster are tuned by the Quasi-Newton (QN) algorithm to make the action sequence of CMAS optimal. Such a policy of CMAS effectively manipulates the inputs of the AHU, where the agents of the AHU activate the natural ventilation and set chillers into an idle state when the outdoor temperature crosses the recommended value. So, it is reasonable to assess the impact potential of thermal mass and hybrid ventilation strategy in reducing cooling energy; accordingly, the assigning results of the proposed DCLTML show that its main cooling coil saves >40% compared to the conventional benchmarks. Besides significant energy savings and improving environmental comfort, the DCLTML exhibits superior high-speed response and robustness performance and eliminates fatigue and wear due to shuttering valves. The results show that the DCLTML algorithm is a promising new approach for controlling HVAC systems. It is more robust to environmental variations than traditional controllers, and it can learn to control the HVAC system in a way that minimises energy consumption. The DCLTML algorithm is still under development, but it can potentially revolutionise how HVAC systems are controlled. [Display omitted] • Clustering based DCLTML structure is used to tackle an extremely large state-action space. • DCLTML provides a viable approach to handling high-dimensional dataset and massive data. • Clustering technique generated to represent a Lagrangian formula for agent action. • The quasi-Newton algorithm is a well-fitting of Lagrangian formula to best agent action. • The results for the DCLTML show saving >40% of main cooling coil energy. [ABSTRACT FROM AUTHOR]

[Display omitted] • Clustering based hybrid network structure is used to tackle an extremely large state-action space. • Converting the TS inference into hybrid layers enables HDCMARL to deal with the continuous actions space. • Clustering structure generated by novel TSF rules for systemizing multi-agent policy. • Quasi-Newton algorithm is well tuning the parameters and weights of policy for storing at hybrid layers. • The investigation for the HDCMARL performance saving more than 32% of HVAC energy. The heating, ventilating and air conditioning (HVAC) systems energy demand can be reduced by manipulating indoor conditions within the comfort range, which relates to control performance and, simultaneously, achieves peak load shifting toward off-peak hours. Reinforcement learning (RL) is considered a promising technique to solve this problem without an analytical approach, but it has been unable to overcome the awkwardness of an extremely large action space in the real world; it would be quite hard to converge to a set point. The core of the problem with RL is its state space and action space of multi-agent action for building and HVAC systems that have an extremely large amount of training data sets. This makes it difficult to create weights layers accurately of the black-box model. Despite the efforts of past works carried out on deep RL, there are still drawback issues that have not been dealt with as part of the basic elements of large action space and the large-scale nonlinearity due to high thermal inertia. The hybrid deep clustering of multi-agent reinforcement learning (HDCMARL) has the ability to overcome these challenges since the hybrid deep clustering approach has a higher capacity for learning the representation of large space and massive data. The framework of RL agents is a greedy iterative trained and organized as a hybrid layer clustering structure to be able to deal with a non-convex, non-linear and non-separable objective function. The parameters of the hybrid layer are optimized by using the Quasi-Newton (QN) algorithm for fast response signals of agents. That is to say, the main motivation is that the state and action space of multi-agent actions for building HVAC controls are exploding, and the proposed method can overcome this challenge and achieve 32% better performance in energy savings and 21% better performance in thermal comfort than PID. [ABSTRACT FROM AUTHOR]

In the domestic sector, increased energy consumption of home appliances has become a growing issue. Thus, reducing and scheduling energy usage is the key for any home energy management system (HEMS). To better match demand and supply, many utilities offer residential demand response program to change the pattern of power consumption of a residential customer by curtailing or shifting their energy use during the peak time period. In the present study, real time optimal schedule controller for HEMS is proposed using a new binary backtracking search algorithm (BBSA) to manage the energy consumption. The BBSA gives optimal schedule for home devices in order to limit the demand of total load and schedule the operation of home appliances at specific times during the day. Hardware prototype of smart sockets and graphical user interface software were designed to demonstrate the proposed HEMS and to provide the interface between loads and scheduler, respectively. A set of the most common home appliances, namely, air conditioner, water heater, refrigerator, and washing machine has been considered to be controlled. The proposed scheduling algorithm is applied under two cases in which the first case considers operation at weekday from 4 to 11 pm and the second case considers weekend at different time of the day. Experimental results of the proposed BBSA schedule controller are compared with the binary particle swarm optimization (BPSO) schedule controller to verify the accuracy of the developed controller in the HEMS. The BBSA schedule controller provides better results compared to that of the BPSO schedule controller in reducing the energy consumption and the total electricity bill and save the energy at peak hours of certain loads. [ABSTRACT FROM AUTHOR]