Search

Thermal insulation coating (TIC) is beneficial for reducing building energy consumption. In this work, a joint optimization and analysis approach for multi-parameters of TIC was proposed to determine its optimization trend, importance order of parameter and available parameter combinations. Its radiation characteristics and thermal resistance were comprehensively considered. Based on the approach, TIC was evaluated from energy saving rate of cooling (ESR cool), heating (ESR heat), and total energy consumption (ESR total) in two climates: hot summer cold winter (Shanghai), and hot summer warm winter (Guangzhou). Results indicated that constantly positive ESR cool was only achieved by optimizing solar absorptivity, while constantly positive ESR heat can be obtained by optimizing different parameters. To guarantee positive ESR total , thermal resistance should exceed 0.11 m2·K·W−1 or solar absorptivity should be between 0.2 and 0.6 in Shanghai, while solar absorptivity should be between 0.1 and 0.3 in Guangzhou. For these two cities, the most important parameter was thermal resistance or solar absorptivity. The optimal TIC for Shanghai was "high thermal resistance + low solar absorptivity + low emissivity", while high emissivity was suitable for Guangzhou. By utilizing the approach and results, energy-saving effect of TIC can be optimized during material design, and influence of material aging can be predicted. • A joint optimization and analysis approach for multi-parameters of TIC is proposed. • Optimization and deterioration impact of TIC multiple parameters is evaluated. • Parameter combination law for positive or negative energy saving rate is obtained. • Importance order of parameter is used for optimization design and material aging. • Effect of energy consumption caused by material aging consequences is predicted. [ABSTRACT FROM AUTHOR]

Most existing material design studies assume that radiative cooling (RC) windows should be ideal radiators with high emissivity (ε λ ∼ 1) in wide infrared band (IR, 2.5–20 μm). However, whether this assumption is universal has not been tested. Different from previous studies based on experiments and steady-state theoretical calculation models, a transient building heat transfer model with facade windows verified by experiments is used to explore the optimal design of IR emissivity of RC windows. Results show that the RC effect of windows is not always improved as traditionally expected with the increase of emissivity ( ε ‾ NAW) in the infrared " non-atmospheric window (NAW) " band. In fact, the adjustment trend of ε ‾ NAW for the RC effect of windows is reversed. Smaller ε ‾ NAW can weaken the heat flow into the room and prolong the time that windows become " coolers ". A wider range of window-to-wall ratios are also studied, and it is found that the design of selective high emissivity ( ε ‾ NAW ∼0) is more conducive to the RC of windows. It is proved that the current default design of the RC window emissivity in IR band is non-universal, and new laws found provide an enlightening guide for the material development of RC windows. • A reversal trend of RC regulation of window emissivity outside the AW is revealed. • Smaller window emissivity outside the AW extends the time windows become coolers. • Designs with selective high emissivity may be more favorable for the RC of windows. • The default "broadband, high emissivity" of RC windows in IR band is not universal. [ABSTRACT FROM AUTHOR]

Piezoelectric energy harvesters (PEH) are widely used to collect piezoelectric energy. However, adapting to different engineering application environments is difficult for traditional PEHs. To improve the engineering application value of PEH, a hanger piezoelectric energy harvester (HPEH) based on low frequency vibration of hanger is proposed in this paper. A dynamic electromechanical coupling model was established for hanger structure, and the effects of the external load resistance, the polyvinylidene fluoride (PVDF) piezoelectric film installation location, the stability of the PVDF piezoelectric film with different winding methods, and the external excitation amplitude on the energy output performance of the HPEH were studied. Results indicate that the optimal load resistance of the HPEH studied in this paper is 1.0 MΩ; the PVDF piezoelectric film location has a significant impact on the performance of the HPEH, and the output power is highest at hanger L c/4; the PVDF piezoelectric films during ring winding is better than those in spiral winding for the same external cycle excitation; in the external excitation amplitude range of 2 d –6 d (d : hanger diameter), appropriately increasing the external excitation amplitude is conducive to improving the output voltage of the HPEH, with the root mean square voltage increasing by 95.11 % at external excitation amplitude A = 6 d compared with that at the initial excitation amplitude. It provides an innovative solution for the field of cable structures and new energy sources, and the further research and application of this technology is expected to have a profound impact in the field of energy sustainability and engineering technology. • This paper proposes a novel hanger piezoelectric energy harvester. • The key parameters affecting the energy harvesting performance are analyzed. • The engineering application value is increased compare with conventional harvesters. • It is suitable for low-frequency vibrations of cable structures. • It provides a new idea for energy harvesting of cable structures. [ABSTRACT FROM AUTHOR]

Deep reinforcement learning (DRL) has been widely studied in the energy management of hybrid electric vehicles (HEVs) for its remarkable energy efficiency improvement compared to conventional methods. However, how to alleviate the time consumption of training a stable reinforcement learning agent still needs to be solved in real-world implementation. This study presents a human expert knowledge encoded 'warm start' method with the flexibility to change the neural network architecture. The expert knowledge is encoded in a decision tree which then initializes the weights and bias of the DRL neural network. Compared with another fixed architecture warm start method, the proposed FlexNet exhibits improved learning speed by 60.8 % and 88.8 % in action space 50 and 100, respectively. The energy consumption by the proposed FlexNet EMS method is 12.2 % and 6.4 % better than rule-based and equivalent consumption minimization strategy, respectively. This proposed warm start method can reduce learning time and increase energy efficiency in various energy management applications. • Expert knowledge is encoded in a decision tree to warm-start reinforcement learning. • The expert knowledge encode process allows flexible neural network architecture (FlexNet). • The proposed FlexNet reduces learning time by 60–80 % compared with existing method. • The proposed FlexNet reduces energy consumption compared with baseline methods. [ABSTRACT FROM AUTHOR]