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This paper focused on the impact of window shades’ physical characteristics and opening modes on annual air conditioning energy consumption of residential buildings in China. Three building models with different window layouts were proposed initially, and then validated by the dimensionless indicator annual energy performance (AEP) to determine the final building model adopted in this study. Harbin, Beijing, Guangzhou, Changsha and Kunming were selected to represent five different climate zones in China. The shading model has taken six physical characteristics of window shades, viz. outer emissivity, inner emissivity, solar transmittance, solar reflectance, thermal infrared transmittance, conductivity and two opening modes, viz. up & down mode as well as left & right mode, into consideration. Besides, the window shades operation schedule was summarized through 949 valid questionnaires. The annual air conditioning energy consumption was calculated via EnergyPlus software, and then the impact of the above different opening modes and parameters on energy use were investigated. The analysis results indicated that the top & bottom opening mode is better than the left & right mode when used in Harbin, and the effect of two different modes in air conditioning energy consumption in Kunming is similar. But in the other three cities, the left and right mode is preferred. Besides, the analysis results of the impact of different physical characteristics on air conditioning energy use in different climate zones could help improve the energy performance of window shades.

This paper investigated the energy performance of window attachments and analyzed the impact of window attachments’ parameters on air conditioning energy consumption of residential buildings in a cooling-dominated city (Houston) and a heating-dominated city (Minneapolis). Roller and cellular shades were selected as research objects in this study. Nine parameters related to window attachment energy performance were chosen as the probable important influence factors for the parametric study. Three values, high, medium and low levels, were set for each parameter, resulting in a total of 9520 (roller) and 9072 (cellular) combinations respectively. The optical and thermal properties of each combination were calculated with Berkeley Lab WINDOW program before coupling with an EnergyPlus typical residential building model. Based on simulation results, an annual energy performance (AEP) index was proposed to rate the energy saving potential of window attachments. Key parameters which have significant impacts on air conditioning energy consumption were determined through the parameter sensitivity analysis. The research results indicated that solar transmittance and solar reflectance are the most influential factors to the energy performance of window attachments among the nine selected parameters. The current cellular shade could achieve the AEPh and AEPc of 0.19 and 0.12. Among all combinations, the best AEPh and AEPc of cellular shades are 0.68 and 0.89, respectively. Besides, as for roller shades, the current level of AEPh and AEPc are 0.14 and 0.09, and the optimal AEPh and AEPc are 0.43 and 0.79.

This paper presents a comprehensive parametric study on the energy-saving potential of venetian blinds in residential buildings. Nine parameters which may affect the energy performance of venetian blinds were selected for parametric analysis. Three values with high, medium and low levels were set for each parameter. The medium value was set according to the mainstream products on the market, while the high and low values were the theoretical upper and lower limits. Thus, a combination of 9072 cases of venetian blinds were obtained. The above cases were modeled in WINDOW software with 0°, 45°, −45° and 90° blind angles, respectively. The EnergyPlus software was used to simulate annual air-conditioning energy consumption for each case. Finally, a new index, annual energy performance (AEP), is developed for evaluating the energy-saving potential of venetian blinds.

Buildings account for 40% of global energy consumption, while windows are the least energy-efficient part of buildings. Conventional smart windows only regulate solar transmission. For the first time, we developed high thermal energy storage thermochromic smart window (HTEST smart window) by trapping the liquid hydrogel within triple-glazed glass. The outstanding specific heat capacity in liquid hydrogel together with excellent thermochromic property (90% of luminous transmittance and 68.1% solar modulation) and good thermal insulation gives the HTEST smart window excellent energy storage and conservation performance. Simulations suggested that HTEST window can save 15.3% more energy than the double-glazed low-E glass. In outdoor demonstrations, the HTEST smart window significantly lowered the room temperature ~30°C in summer compared with the double-glazed glass. Compared with conventional energy-saving glasses which need expensive equipment, the “water” trapped structure offers a disruptive strategy of easy fabrication and scale-up, which opens an avenue for energy-saving buildings and greenhouses.

Summary Architectural windows that smartly regulate the indoor solar irradiation are promised to economize the building energy consumption. Here, we demonstrate a method for adaptive, broadband, and highly efficient solar modulation for energy-efficient smart windows through active plasmonics in reconfigurable structures. We develop a kirigami-inspired elastomer containing plasmonic vanadium dioxide (VO2) nanoparticles, in which the geometrical transition and the temperature-dependent localized surface plasmon resonance (LSPR) present dominant controls in ultraviolet-visible and near-infrared regions, respectively. The active LSPR control, via stretch-induced local dielectric changes is mitigated on reconfigurable metamaterials because of their unique strain distributions. This method demonstrated a desirable property in energy-efficient smart windows facilitating improved solar energy modulation (37.7%), surpassing the best-reported modulation in passive and transparent VO2 thermochromism systems. This first attempt to integrate the plasmonics and reconfigurable structures may inspire developments in smart windows, building energy economization, as well as fundamental studies of plasmonic controls in metastructures.

In this paper, we focus on the task of fine-grained text sentiment transfer (FGST). This task aims to revise an input sequence to satisfy a given sentiment intensity, while preserving the original semantic content. Different from the conventional sentiment transfer task that only reverses the sentiment polarity (positive/negative) of text, the FTST task requires more nuanced and fine-grained control of sentiment. To remedy this, we propose a novel Seq2SentiSeq model. Specifically, the numeric sentiment intensity value is incorporated into the decoder via a Gaussian kernel layer to finely control the sentiment intensity of the output. Moreover, to tackle the problem of lacking parallel data, we propose a cycle reinforcement learning algorithm to guide the model training. In this framework, the elaborately designed rewards can balance both sentiment transformation and content preservation, while not requiring any ground truth output. Experimental results show that our approach can outperform existing methods by a large margin in both automatic evaluation and human evaluation.

The existing calculation and evaluation methods for photovoltaic directly driven air conditioners (PVAC) are often based on a long timescale without considering the short-term rapid fluctuations of environmental parameters. This study proposes a comprehensive method for evaluating real-time zero energy potentials of PVACs. Firstly, an optimal determination method for PV size from the timely energy matching perspective was discussed. Then, the energy generation and consumption based on different time scales in different climate regions were simulated and analyzed. Finally, the zero-energy potentials of PVACs with the optimal PV size in different climate regions were investigated. The potentials were evaluated based on the indices of hourly self-sufficiency, hourly self-consumption, zero energy probability, and PV generation utilization. Besides these indicators, a graphical approach was also provided to enable the visualization of the real-time energy matching performance of PVACs. The results showed that different climatic conditions had significant effects on energy matching performance. Under the premise that the total PV generation can fully meet the electricity consumption of air conditioner, the number of hours that PVAC, without assistance, can realize real-time zero energy consumption only accounts for 26.40% of the whole year in Shenyang and 37.17% in Shanghai. To summarize, the method and indicators for optimizing PV capacity and evaluating dynamic energy matching for different climate zones were proposed from the perspective of achieving real-time zero energy.

Architectural windows that smartly regulate the indoor solar irradiation are promised to economize the building energy consumption. Here we demonstrate a method for adaptive, broadband, and high-efficient solar modulation for energy-efficient smart windows through the active plasmonics in kirigami structures. We develop a kirigami-inspired elastomer containing plasmonic vanadium dioxide (VO2) nanoparticles, in which the geometrical transition and the temperature-dependent localized surface plasmon resonance (LSPR) present dominant controls in ultraviolet-visible and near-infrared regions, respectively. The active LSPR control via stretch-induced local dielectric changes is mitigated on kirigami metamaterials due to their unique strain distributions. This method was demonstrated a decent property in energy-efficient smart windows with a decent solar energy modulation (37.7%), surpassing the best reported passive and transparent VO2 thermochromism systems. This first attempt to integrate the plasmonics and kirigami may inspire developments in smart windows, building energy economization, as well as fundamental studies of plasmonic controls in metastructures.

Word Sense Disambiguation (WSD), as a tough task in Natural Language Processing (NLP), aims to identify the correct sense of an ambiguous word in a given context. There are two mainstreams in WSD. Supervised methods mainly utilize labeled context to train a classifier which generates the right probability distribution of word senses. Meanwhile knowledge-based (unsupervised) methods which focus on glosses (word sense definitions) always calculate the similarity of context-gloss pair as score to find out the right word sense. In this paper, we propose a generative adversarial framework WSD-GAN which combines two mainstream methods in WSD. The generative model, based on supervised methods, tries to generate a probability distribution over the word senses. Meanwhile the discriminative model, based on knowledge-based methods, focuses on predicting the relevancy of the context-gloss pairs and identifies the correct pairs over the others. Furthermore, in order to optimize both two models, we leverage policy gradient to enhance the performances of the two models mutually. Our experimental results show that WSD-GAN achieves competitive results on several English all-words WSD datasets.