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The tea plant, Camellia sinensis (L.) O. Kuntze, is an economically important, perennial woody plant rich in catechins. Although catechins have been reported to play an important role in plant defences against microbes, their roles in the defence of tea plants against herbivores remain unknown. In this study, we allowed the larvae of Ectropis grisescens, a leaf-feeding pest, to feed on the plants, and alternatively, we wounded the plants and then treated them with E. grisescens oral secretions (WOS). Both approaches triggered jasmonic acid-, ethylene- and auxin-mediated signalling pathways; as a result, plants accumulated three catechin compounds: (+)-catechin, epicatechin and epigallocatechin. Not only was the mass of E. grisescens larvae fed on plants previously infested with E. grisescens or treated with WOS significantly lower than that of larvae fed on controls, but also artificial diet supplemented with epicatechin, (+)-catechin or epigallocatechin gallate reduced larval growth rates. In addition, the exogenous application of jasmonic acid, ethylene or auxin induced the biosynthesis of the three catechins, which, in turn, enhanced the resistance of tea plants to E. grisescens, leading to the coordination of the three signalling pathways. Our results suggest that the three catechins play an important role in the defences of tea plants against E. grisescens.

Accurate power load forecasting can facilitate effective distribution of power and avoid wasting power so as to reduce costs. Power load is affected by many factors, so accurate forecasting is more difficult, and the current methods are mostly aimed at short-term power load forecasting problems. There is no good method for long-term power load forecasting problems. Aiming at this problem, this paper proposes an LSTM-Informer model based on ensemble learning to solve the long-term load forecasting problem. The bottom layer of the model uses the long short-term memory network (LSTM) model as a learner to capture the short-term time correlation of power load, and the top layer uses the Informer model to solve the long-term dependence problem of power load forecasting. In this way, the LSTM-Informer model can not only capture short-term time correlation but can also accurately predict long-term power load. In this paper, a one-year dataset of the distribution network in the city of Tetouan in northern Morocco was used for experiments, and the mean square error (MSE) and mean absolute error (MAE) were used as evaluation criteria. The long-term prediction of this model is 0.58 and 0.38 higher than that of the lstm model based on MSE and MAE. The experimental results show that the LSTM-Informer model based on ensemble learning has more advantages in long-term power load forecasting than the advanced baseline method.

Plant cystatins play crucial roles in the process of plant defense against herbivorous insects. A cDNA clone, designated CsCPI2, was isolated from Camellia sinensis cv. Longjing 43 using 5′-/3′-RACE extension. The full-length cDNA gene is 618 bp in size, encodes 205 amino acid residues, and has a deduced molecular weight of 23.07 kDa. CsCPI2 was different from the already published CsCPI1, as the two cDNA sequences share 69.31% and 60.4% pairwise identity at the nucleic acid and amino acid levels, respectively. The CsCPI2–MBP fusion protein was over-expressed in Escherichia coli. We found that the inhibitory effect of CsCPI2–MBP on ficin was 1.17, 3.46, and 3.73 times the effect on papain, chymopapain, and bromelain, respectively. The highest inhibitory activity of CsCPI2–MBP was found at pH 7 at 40 °C, and CsCPI2–MBP was stable at temperature below 60 °C. Moreover, CsCPI2–MBP was found to be more responsive to acidic environment than to alkaline one. Higher cystatin inhibitory activity was found in the tea leaves infested by Myllocerinus aurolineatus than that in intact healthy leaves. Finally, we found that CsCPI2 was a competitive inhibitor of the cysteine proteinases in tea weevil’s gut, and leaves treated with CsCPI2–MBP incurred compensatory damage, and the weight gain of tea weevils fed on CsCPI2–MBP treated leaves was significantly higher than that of MBP treated leaves, but did not cause mortality. Taken together, our research highlights potential defensive proteins for future tea engineering approaches, but more in vivo experiments are needed to confirm their effectiveness.

The jasmonic acid (JA) signaling pathway plays a vital role in mediating plant resistance to herbivores. Tea plant (Camellia sinensis) is one of the most important woody cash crops in the world. Due to the lack of genetic transformation systems for tea plants, how the JA signaling pathway works in tea plants has not yet been determined. Now, with the development of cross-disciplines, chemical biology provides new means for analysing the JA signaling pathway. In the present study, the small molecule isoquinoline compound ZINC71820901 (lyn3) was obtained from the ZINC molecular library through virtual screening based on the structure of the crystal COI1-JAZ1 co-receptor and was found to act as an inhibitor of the JA signaling pathway both in Arabidopsis and tea plants. Our results revealed that lyn3 repressed tea plant resistance to Ectropis grisescens mainly by decreasing the accumulation of (-)-epicatechin (EC) and (-)-epigallocatechin (EGC) via repression of the JA signaling pathway, which functioned in the different modulation manner to the already known inhibitor SHAM. As a novel inhibitor of JA signaling pathway, lyn3 provides a specific option for further research on the JA pathway.

The jasmonic acid (JA) signaling pathway plays a vital role in mediating plant resistance to herbivores. The tea plant (Camellia sinensis) is one of the most important woody cash crops in the world. Due to the lack of genetic transformation systems for tea plants, how the JA signaling pathway works in tea plants has not yet been determined. Now, with the development of cross-disciplines, chemical biology provides new means for analyzing the JA signaling pathway. In the present study, the structure of the small-molecule isoquinoline compound ZINC71820901 (lyn3) was obtained from the ZINC molecular library through virtual screening based on the structure of the crystal COI1-JAZ1 co-receptor and was found to act as an inhibitor of the JA signaling pathway in both Arabidopsis and tea plants. Our results revealed that lyn3 repressed tea plant resistance to Ectropis grisescens mainly by decreasing the accumulation of (−)-epicatechin and (−)-epigallocatechin via repression of the JA signaling pathway, which functioned in a modulation manner different from that of the already known inhibitor salicylhydroxamic acid (SHAM). As a novel inhibitor of the JA signaling pathway, lyn3 provides a specific option for further research on the JA pathway.

Gallic acid (GA), an important polyphenolic compound in the plant, is a well-known antioxidant, antihyperglycemic, and anti-lipid peroxidative agent. Recently, GA treatment exhibited ameliorative effects on plants in response to some abiotic stresses. However, the elicitation effect of GA on plant defense against herbivorous insects has not yet been reported. In this study, we found that the exogenous application of GA induced the direct defense of tea plant (

Power system plan, dispatch and control is based on the prediction accuracy of power time series data. To improve the prediction accuracy of power system time series, this paper proposes an improved particle swarm algorithm to optimize the time series prediction model of long and short-term memory neural network. Aiming at the disadvantage that traditional particle swarm optimization has a long optimization time and easy to fall into local optimum, an improved particle swarm optimization (IPSO) is proposed. By dynamically changing the size of the inertia weight and acceleration factor, the global optimization capability and local convergence speed of the particle swarm algorithm are improved. The experimental results show that, compared with the traditional long and short-term memory neural network, the IPSO-LSTM model shows a good predictive effect on power time series data and has a stronger generalization ability.

The selection of reliable reference genes (RGs) for normalization under given experimental conditions is necessary to develop an accurate qRT-PCR assay. To the best of our knowledge, only a small number of RGs have been rigorously identified and used in tea plants (Camellia sinensis (L.) O. Kuntze) under abiotic stresses, but no critical RG identification has been performed for tea plants under any biotic stresses till now. In the present study, we measured the mRNA transcriptional levels of ten candidate RGs under five experimental conditions; these genes have been identified as stable RGs in tea plants. By using the ΔCt method, geNorm, NormFinder and BestKeeper, CLATHRIN1 and UBC1, TUA1 and SAND1, or SAND1 and UBC1 were identified as the best combination for normalizing diurnal gene expression in leaves, stems and roots individually; CLATHRIN1 and GAPDH1 were identified as the best combination for jasmonic acid treatment; ACTIN1 and UBC1 were identified as the best combination for Toxoptera aurantii-infested leaves; UBC1 and GAPDH1 were identified as the best combination for Empoasca onukii-infested leaves; and SAND1 and TBP1 were identified as the best combination for Ectropis obliqua regurgitant-treated leaves. Furthermore, our results suggest that if the processing time of the treatment was long, the best RGs for normalization should be recommended according to the stability of the proposed RGs in different time intervals when intragroup differences were compared, which would strongly increase the accuracy and sensitivity of target gene expression in tea plants under biotic stresses. However, when the differences of intergroup were compared, the RGs for normalization should keep consistent across different time points. The results of this study provide a technical guidance for further study of the molecular mechanisms of tea plants under different biotic stresses.

Previous research has shown that under-ventilation of classrooms is common and negatively impacts student health and learning. To advance understanding of contributing factors, this study visited 104 classrooms from 11 schools that had recently been retrofitted with new heating, ventilation, and air-conditioning (HVAC) units. CO2 concentration, room and supply air temperature and relative humidity, and door opening were measured for four weeks in each classroom. Field inspections identified HVAC equipment, fan control, and/or filter maintenance problems in 51% of the studied classrooms. Across 94 classrooms with valid data, average CO2 concentrations measured during school hours had a mean of 895 ppm and a standard deviation (SD) of 263 ppm. Ventilation rates (VRs), estimated using the daily maximum 15-min CO2 in each classroom, had a mean of 5.2 L/s-person and a SD of 2.0 L/s-person across 94 classrooms. Classrooms with economizers, with or without demand control ventilation (DCV), tended to have lower mean CO2. Improperly selected equipment, lack of commissioning, incorrect fan control settings and maintenance issues (heavily loaded filters) were all associated with under-ventilation in classrooms. Many classrooms in this sample were frequently too warm to support learning. There were 23 out of 103 classrooms that had indoor air temperature above 25.6 °C for more than 20% of the school hours. Better oversight on HVAC system installation and commissioning are needed to ensure adequate classroom ventilation. Periodic testing of ventilation systems and/or continuous real-time CO2 monitoring (either as stand-alone monitors or incorporated into thermostats) is recommended to detect and correct ventilation problems.

In this paper, an intelligent ensemble machine learning (EML) method - Bagging was developed for thermal perception prediction. Field data was collected in naturally ventilated (NV) and split air-conditioning (SAC) dormitory buildings in hot summer and cold winter (HSCW) area of China during the summer of 2016. The indoor physical measurement and subjective survey were conducted simultaneously during the field study. To determine the merit of the proposed Bagging approach, the performances of Bagging approach were compared against the artificial neural network (ANN) and support vector machine (SVM) regarding conventional statistical indicators, i.e., mean absolute error (MAE), root mean square error (RMSE), coefficient of determination (R2) and Pearson correlation coefficient (r). Several thermal indices, i.e., thermal sensation (TS), effective temperature (ET*), standard effective temperature (SET*) and predicted mean vote (PMV), were adopted to access the occupants’ thermal comfort and evaluate the model prediction. In our case study, the Bagging model's R2 for TS, PMV, ET* and SET* were 0.4986, 0.9892, 0.9920 and 0.9900, respectively. It shows higher accuracy than SVM and ANN models in thermal perception prediction and outperforms the classical PMV index in TS prediction. Results indicate the proposed Bagging model's prediction performance is reliable and is highly accurate to predict the thermal perception.

An efficient catalytic asymmetric chlorocyclization of olefinic amides with 1,3-dichloro-5,5-dimethylhydantoin (DCDMH) using hydroquinidine 1,4-phthalazinediyl diether ((DHQD)2PHAL) as organocatalyst has been developed. Series of chiral chloro substituted isobenzofuran-1(3H)-imine derivatives were obtained in good yields (up to 85%) and enantioselectivities (up to 70% ee).

The emergence of smart grids, Net-zero energy buildings, and advanced building energy demand response technologies continuously drives the needs for better design and operation strategies for buildings and distributed energy systems. It is envisioned that similar to micro-communities in a human society, neighboring buildings will have the tendency to form a building cluster, an open cyber-physical system to exploit the economic opportunities provided by smart grids and distributed energy systems. To realize this building cluster envision, it requires better urban energy planning and operation control strategies to determine which type of buildings should be clustered and what operation strategies should be implemented to fully utilize the potential in load aggregation, load shifting, and resource allocation. However, most of the current tools are focusing on single buildings or devices, which are not suitable for building cluster studies. To this end, this study proposes to develop a Net-zero building cluster emulator that can simulate realistic energy behaviors of a cluster of buildings and their distributed energy devices as well as exchange operation data and control schemes with real-world building control systems. The developed emulator has the flexibility to integrate with different buildings and distributed energy systems to study the performance of this building cluster to propose suggestions in urban energy planning and operation. To show the application of this emulator, a proof-of-concept demonstration is also presented in this paper.

There is usually discrepancy between deterministic modeling and actual operations of HVAC (heating, ventilation and air-conditioning) systems to some extent depending on the model comprehensiveness. Our main challenge is to express associated uncertainty in model outcomes in a way that is helpful to those trying to use the predictions from simulations to make decisions. Considerable research is underway to quantify uncertainty in building simulation. A recent trend is to include HVAC system parameters in uncertainty analysis. Outdoor air control has significant impact on energy consumption and indoor air quality. This study investigates the uncertainties of outdoor air control of a particular system type. Based on the analysis, the authors have identified the uncertain parameters, quantified their uncertainty patterns and mapped them to building performance simulation in terms of energy consumption and ventilation requirement. The simulated results show that the uncertainty in outdoor air control can lead to 17% difference in cooling consumption and 43% difference in heating consumption compared with using standard outdoor air control settings in EnergyPlus simulation. The effects of the identified uncertainty on fault detection are also discussed.

In this study, we reported the synthesis and biological characterization of a novel series of furan-carboxamide derivatives that were potent inhibitors of the influenza A H5N1 virus. The systematic structure–activity relationship (SAR) studies demonstrated that the 2,5-dimethyl-substituted heterocyclic moiety (furan or thiophene) had significant influence on the anti-influenza activity. In particular, 2,5-dimethyl-N-(2-((4-nitrobenzyl)thio)ethyl)-furan-3-carboxamide 1a showed the best activity against the H5N1 virus with an EC50 value of 1.25 μM. For the first time, the simple scaffold furan-carboxamide derivatives were identified as novel inhibitors of lethal H5N1 influenza A virus.

Polyphenol oxidases (PPOs) as inducible defense proteins, contribute to tea (Camellia sinensis) resistance against tea geometrid larvae (Ectropis grisescens), and this resistance has been associated with the jasmonic acid (JA) signaling by testing geometrid performance in our previous work. However, the regulation of PPO-based defense by JA and other hormone signaling underlying these defense responses is poorly understood. Here, we investigated the role of phytohormones in regulating the PPO response to tea geometrids. We profiled levels of defense hormones, PPO activity and CsPPO genes in leaves infested with tea geometrids. Then, hormone levels were manipulated by exogenous application of methyl jasmonate (MeJA), gibberellin acid (GA3), abscisic acid (ABA), JA biosynthesis inhibitors (sodium diethyldithiocarbamate trihydrate, DIECA and salicylhydroxamic acid, SHAM) and GA inhibitor (uniconazole, UNI). Upon geometrid attack, JA levels significantly increased, whereas GA levels notably decreased and ABA level was slightly decreased. And the PPO activity significantly increased in line with the transcript levels of CsPPO2 and CsPPO4 but not CsPPO1. There were an obvious antagonistic cross-talk between JA and GA signals and an association among JA signals, PPO response and herbivore resistance in tea plants. Pretreatment with MeJA increased PPO activity by activating the transcripts of CsPPO2 and CsPPO4, whereas application of JA inhibitor DIECA suppressed PPO activity. GA3 strongly enhanced PPO activity, but ABA did not alter PPO activity. These findings strongly suggest that JA is a central player in PPO-mediated tea resistance against tea geometrids in a manner that prioritizes defense over growth.

Acetaminophen (APAP)-induced acute hepatotoxicity is the leading cause of drug-induced acute liver failure. The aim of this study was to evaluate the effects of 4-hydroxy-2,2,6,6-tetramethylpiperidine-N-oxyl (tempol) on the protection of APAP-induced hepatotoxicity in mice. Mice were pretreated with a single dose of tempol (20 mg/kg per day) orally for 7 days. On the seventh day, mice were injected with a single dose of APAP (300 mg/kg) to induce acute hepatotoxicity. Our results showed that tempol treatment markedly improved liver functions with alleviations of histopathological damage induced by APAP. Tempol treatment upregulated levels of antioxidant proteins, including superoxide dismutase, catalase, and glutathione. Also, phosphorylation of phosphoinositide 3-kinase (PI3K) and protein kinase B (Akt) and protein expression of nuclear factor erythroid 2-related factor (Nrf 2) and heme oxygense-1 (HO-1) were all increased by tempol, which indicated tempol protected against APAP-induced hepatotoxicity via the PI3K/Akt/Nrf2 pathway. Moreover, tempol treatment decreased pro-apoptotic protein expressions (cleaved caspase-3 and Bax) and increased anti-apoptotic Bcl-2 in liver, as well as reducing apoptotic cells of TUNEL staining, which suggested apoptotic effects of tempol treatment. Overall, we found that tempol normalizes liver function in APAP-induced acute hepatotoxicity mice via activating PI3K/Akt/Nrf2 pathway, thus enhancing antioxidant response and inhibiting hepatic apoptosis.

In this paper, we report on the indoor concentrations from a suite of full-scale outdoor tracer-gas point releases conducted in the downtown area of Oklahoma City in 2003. A point release experiment consisted of releases of sulfur hexafluoride (SF6 ) in multiple buildings and from different outdoor locations. From the measurements, we are able to estimate the concentration variations indoors for a building operating under "typical" operating conditions. The mean indoor spatial coefficients of variation are 30% to 45% from a daytime outdoor release are around 80% during an outdoor evening release. Having estimates of the spatial coefficient of variation provides stakeholders, including first responders, with the likely range of concentrations in the building when little is known about the building characteristics and operating behavior, such as developing urban-scale hazard and consequence analyses. We show differences in indoor measurements at different distances to the release points, floors of the building, and heating, ventilation, and air conditioning system (HVAC) operation. We also show estimates at different time resolutions. The statistics show that in the studied medium to large commercial buildings, spatial differences would result in peak indoor concentrations in certain parts of the buildings that may be substantially higher than the building average. To our knowledge, very few tracer gas measurements have been conducted in buildings of this scope, particularly with measurements on multiple floors and within a floor. The resulting estimates of spatial variability provide a unique opportunity for hazard assessment, and comparison to multi-zone models.

Author(s): Less, Brennan; Dutton, Spencer; Li, Xiwang; Clark, Jordan; Walker, Iain; Sherman, Max | Abstract: This study is intended to demonstrate the potential for energy savings while providing acceptable Indoor Air Quality (IAQ) for ZNE homes. It uses the concept of Smart Ventilation where ventilation systems are designed and controlled to produce the same, or better, IAQ compared to simple, continuously operated ventilation systems. The key energy saving principle for smart ventilation is that ventilation is shifted in time to when the energy required to condition the air is lower. A variety of smart ventilation controls based on outdoor temperature, occupancy and auxiliary fan sensing were developed and assessed across homes built to the 2016 Title 24 Prescriptive standards in California climate regions. Simulations used a co-simulation strategy that combines EnergyPlus with CONTAM. The IAQ calculations were based on the equivalent ventilation principle outlined in the ASHRAE 62.2-2016 ventilation standard, Appendix C. Two prototype homes were simulated (1-story 2,100 ft2 and 2-story 2,700 ft2). Their envelope airtightness was varied between 1, 3 and 5 ACH50. Climate zones were chosen to reflect the variety of heating and cooling demand throughout California. A weighted average analysis was used to generalize the energy predictions across the projected new housing stock in the state. Temperature-based controls were found to be effective, with the most successful smart controls reducing weighted average site ventilation energy use by about 40%, while TDV weighted average ventilation energy reductions were higher, up to roughly 60%. Results were also normalized to ensure identical IAQ in all cases, and the weighted average site and TDV ventilation savings increased, up to 55% and 72% ventilation savings, respectively, for the top-performing temperature-based controls. Peak demand during the 2-6pm period on the hottest days of the year was reduced by up to 400 watts. More than 90% of site energy savings were for heating end-uses, while TDV energy savings were split fairly evenly between heating and cooling. On average, the smart controls reduced occupant pollutant exposure by 0-10%, and they increased ventilation rates by roughly 20%. Occupancy-based controls that accounted for contaminants released by building materials and furnishings during unoccupied times were generally ineffective, with very low energy savings. Performance was improved somewhat through use of a 1-hour pre-occupancy flush out period, though savings were still marginal compared to temperature-based controls. All temperature and occupancy controls were also tested with auxiliary fan sensing capability (i.e., accounting for the use of other exhaust devices in the home, like bathroom or kitchen fans). Auxiliary fan sensing increased energy savings in all cases, from roughly 5 to 15%.

Building energy consumption accounts for 30% of the overall energy end use worldwide. This number is even much higher in urban areas. With rapid urbanization in China, cities are expanding with new constructions. It is essential to create an updated urban-scale building energy consumption database to represent energy use for different types of buildings in China, which could help urban planners, managers and decision makers to understand temporal and spatial building energy consumption distribution and ensure required electricity and/or gas supply. However, such urban-scale database is rarely found in China. This paper creates baseline EnergyPlus models for residential, small office and large office buildings and validates the baseline models using survey data from literature. Parametric simulations are conducted to consider different design factors, such as building enclosure, lighting power density, equipment power density, HVAC schedule, etc. In total 351 EnergyPlus models are generated to cover different energy use intensity scenarios. Data-driven regression analysis is conducted to predict building energy consumption using building price/rent. The prediction results are expected to provide design decision support for urban planning and power distribution for new constructions.

Northern cereal mosaic cytorhabdovirus (NCMV) and Barley yellow striate mosaic cytorhabdovirus (BYSMV) are two of the most important viral pathogens of wheat. Northern China is the main wheat-producing region in the country. Wheat growing regions pertaining to four provinces, located in northern China, were surveyed for occurrence of NCMV and BYSMV during the growing seasons of the years 2010 and 2016. Wheat leaf samples were collected randomly from symptomatic plants displaying stunting, chlorotic stripes or mosaic. Roughly 73 samples were collected in the year 2010 from 13 fields, and 154 samples were collected in 2016 from 41 fields. Samples were tested for the presence of NCMV or BYSMV using multiplex reverse transcription-polymerase chain reaction (mRT-PCR). The results suggested that BYSMV (49.32% in 2010, 82.47% in 2016) is gradually replacing NCMV (87.67% in 2010, 13.64% in 2016) and becoming the main cytorhabdovirus in different wheat growing regions in northern China. O cytorhabdovirus do mosaico do cereal do norte (NCMV) e o cytorhabdovirus do mosaico estriado amarelo da cevada (BYSMV) são dois dos mais importantes patógenos virais do trigo. O norte da China é a principalregião produtora de trigo do país. As regiões produtoras de trigo pertencentes a quatro províncias do norte da China foram pesquisadas quanto à ocorrência de NCMV e BYSMV durante as safras dos anos de 2010 e 2016. Amostras de folhas de trigo foram coletadas aleatoriamente de plantas sintomáticas, exibindo listras ou mosaico clorótico com baixo crescimento. Cerca de 73 amostras foram coletadas no ano de 2010 a partir de 13 campos, e 154 amostras foram coletadas em 2016 de 41 campos. As amostras foram testadas quanto à presença de NCMV ou BYSMV usando reação em cadeia da polimerase de transcrição reversa multiplex (mRT-PCR). Os resultados sugerem que o BYSMV (49,32% em 2010, 82,47% em 2016) está gradualmente substituindo o NCMV (87,67% em 2010, 13,64% em 2016) e se tornando o principal cytorhabdovirus em diferentes regiões produtoras de trigo no norte da China.&nbsp

Accurate, computationally efficient, and cost-effective energy forecasting models are essential for model based control. Existing studies in model based control have mostly been focusing on developing energy forecasting models using simplified physics based or data driven models. However, creating and identification the simplified physics model are often challenging, which requires expert knowledge for model simplification and significant engineering efforts for model training. In addition, the accuracy and robustness of data driven models are always bounded by the training data. To this end, developing high fidelity energy forecasting models with less engineering effort and good performance is still an urgent task. Although the previous studies from the authors have shown great promises in a system identification model and outperformed other data-driven and grey box models, they still have large errors at the special operation situations. Therefore, this paper investigates a novel methodology to develop energy estimation models for on-line building control and optimization using an integrated system identification and data fusion approach. The data fusion approach is able to adapt the forecasting model under the special operation situations based on the real measurements. An eigensystem realization algorithm based model reformation method is developed to convert the system identification models into state space models. Kalman filter based data fusion techniques are then implemented on the state space models to improve the model accuracy and robustness. The developed methodology are evaluated using data from a virtual building (simulated) and a real small size commercial building. Three different data fusion intervals: 15, 30, and 60 min, have been tested. The overall building energy estimation accuracy from this proposed methodology can reach to above 95% in the virtual building and around 90% in the real building. The results also show that the shorter data fusion interval used, the higher accuracy can be achieved.

Building thermal mass control has great potentials in saving energy consumption and cost. Optimal control schemes are able to utilize passive thermal mass storage to shift the cooling load from peak hours to off-peak hours to reduce energy costs. As such, this paper explores the idea of model predictive control for building thermal mass control. Specifically, this paper presents a study of developing and evaluating a multi-objective optimization based model predictive control framework for demand response oriented building thermal mass control. This multi-objective optimization framework takes both energy cost and thermal comfort into consideration simultaneously. In this study, the developed model predictive control framework has been applied in six commercial buildings at Boston, Chicago, and Miami, under typical summer weather conditions. Time-of-use electricity prices from these three locations are used to calculate the cooling and reheating energy costs. Pareto curves for optimal temperature setpoints under different thermal comfort requirements are calculated to show the trade-off between the cost saving and thermal comfort maintaining. Comparing with a typical “night setback” operation scheme, this model predictive control schemes are able to save energy costs from 20% to 60% at these three locations under different weather and energy pricing conditions. In addition, the Pareto curves also show that the energy cost saving potentials are highly dependent on the thermal comfort requirements, weather conditions, utility rate structures, and the building constructions.

Driven by the development of smart buildings and smart grids, numerous of research has focused on developing optimal operation strategies for smart buildings with the aims of reducing energy consumption and cost, as well as improving the grid reliability. Unfortunately, most of the studies from smart building perspective only target on a single building with elaborated energy forecasting models. Few of them addresses the effects of multiple buildings on power grid operation. On the other hand, a few studies from smart grid area focus on multiple buildings and their influence on power grid, they usually, however, use simplified linear energy forecasting models, which are hard to guarantee the findings reflecting the cases in real fields. As a result, this research proposes to bridge this research gap, through developing and validating high fidelity energy forecasting models for a building cluster with multiple buildings and distributed energy systems, as well as creating a collaborative operation framework to determining the optimal operation strategies of this building cluster. The operation framework utilizes multi-objective optimizations to determine the operation strategies: building temperature setpoints, energy storage charging and discharging schedules, etc., using particle swarm optimization. Pareto curves for energy cost saving and thermal comfort maintaining are also derived with different thermal comfort requirements. The results from this study show that the developed building cluster collaborative operation framework is able to reduce the energy cost by 12.1–58.3% under different electricity pricing plans and thermal comfort requirements.

Model-based predictive control has been proven to be a promising solution for improving building energy efficiency and building-grid resilience. High fidelity energy forecasting models are essential to the performance of model predictive controls. The existing energy forecasting modeling principles: physics based (white box), data-driven (black box), and hybrid (gray box) modeling principles all have their own limitations in applying into the real field, such as extensive engineering effort, computation power, and long training periods. Previous studies by the authors presented a novel methodology for energy forecasting model development using system identification approaches based on system characteristics. In this study, whole building experiments are systematically designed and conducted to verify and validate this novel method in a real commercial building. The experimental results demonstrate that the proposed methodology is able to achieve 90% forecasting accuracy within a 1-minute calculation time ...

High-fidelity and computationally efficient energy forecasting models for building systems are needed to ensure optimal automatic operation, reduce energy consumption, and improve the building’s resilience capability to power disturbances. Various models have been developed to forecast building energy consumption. However, given buildings have different characteristics and operating conditions, model performance varies. Existing research has mainly taken a trial-and-error approach by developing multiple models and identifying the best performer for a specific building, or presumed one universal model form which is applied on different building cases. To the best of our knowledge, there does not exist a generalized system framework which can recommend appropriate models to forecast the building energy profiles based on building characteristics. To bridge this research gap, we propose a meta-learning based framework, termed Building Energy Model Recommendation System (BEMR). Based on the building’s physical features as well as statistical and time series meta-features extracted from the operational data and energy consumption data, BEMR is able to identify the most appropriate load forecasting model for each unique building. Three sets of experiments on 48 test buildings and one real building were conducted. The first experiment was to test the accuracy of BEMR when the training data and testing data cover the same condition. BEMR correctly identified the best model on 90% of the buildings. The second experiment was to test the robustness of the BEMR when the testing data is only partially covered by the training data. BEMR correctly identified the best model on 83% of the buildings. The third experiment uses a real building case to validate the proposed framework and the result shows promising applicability and extensibility. The experimental results show that BEMR is capable of adapting to a wide variety of building types ranging from a restaurant to a large office, and gives excellent performance in terms of both modeling accuracy and computational efficiency.

Optimal automatic operation of buildings and their subsystems in responding to signals from a smart grid is essential to reduce energy demand, and to improve the power resilience. In order to achieve such automatic operation, high fidelity and computationally efficiency whole building energy forecasting models are needed. Currently, data-driven (black box) models and hybrid (grey box) models are commonly used in model based building control. However, typical black box models often require long training period and are bounded to building operation conditions during the training period. On the other hand, creating a grey box model often requires (a) long calculation time due to parameter optimization process; and (b) expert knowledge during the model development process. This paper attempts to quantitatively evaluate the impacts of two significant system characteristics: system nonlinearity and response time, on the accuracy of the model developed by a system identification process. A general methodology for building energy forecasting model development is then developed. How to adapt the system identification process based on these two characteristics is also studied. A set of comparison criteria are then proposed to evaluate the energy forecasting models generated from the adapted system identification process against other methods reported in the literature, including Resistance and Capacitance method, Support Vector Regression method, Artificial Neural Networks method, and N4SID subspace algorithm. Two commercial buildings: a small and a medium commercial building, with varying chiller nonlinearity, are simulated using EnergyPlus in lieu of real buildings for model development and evaluation. The results from this study show that the adapted system identification process is capable of significantly improve the performance of the energy forecasting model, which is more accurate and more extendable under both of the noise-free and noisy conditions than those models generated by other methods.