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In this paper, the window-opening behavior in a three-star green building with an operation mark in Ningbo was investigated. Four single offices facing south were selected, and the measurement lasted 20–40 days in the cooling season. An analysis of the relationship between window use and environmental factors was assessed by monitoring the occupancy state, the window action and window state, the time, and the temperature and humidity. A statistical analysis method was conducted to reveal the similarities and differences in window use among different occupants. The main findings were as follows: The window adjustment behaviors of different people vary significantly. In terms of adjustment time, there are two modes of behaviors. In addition, the occupancy state had a great impact on window action. Moreover, some people prefer to open the windows in the morning and close the windows at departure time, However, some of them prefer to open the windows at departure time and close the windows at the arrival time. Some irrational window opening behaviors were found in this research. Through correlation analysis, it was shown that the longer time the occupants were in the office, the higher window-opening duration and window-opening frequency would be. If the window was closed at arrival time, the total opening duration of window was significantly reduced. Also, window opening duration and closing duration are proportional to each other. With the help of principal component analysis (PCA) of the 12 features that affected window adjustment, the correlation between the window opening behaviors and the features could be clearly illustrated. A stochastic model of window-opening behavior based on logistics regression and uncertainty analysis was used in this paper. The cooling energy consumption of the stochastic model is 99.9% higher than the energy consumption generated by a fixed number of air changes, and the average energy consumption exceeds the fixed value by more than 21%. The findings of this study have certain guiding significance for promoting energy conservation by occupants' behaviors in green buildings in this region. [ABSTRACT FROM AUTHOR]

Wind energy is an effective way to reduce emissions in ports. However, port wind power generation exhibits strong intermittency and randomness. Predicting port wind speed enables timely scheduling of port operations and improves wind energy utilization efficiency. To achieve high accuracy and rapid prediction of port wind speed, this paper proposes a wind speed prediction model based on the Sparrow Search Algorithm (SSA) optimized Wavelet Neural Network (WNN). Firstly, the SSA is used to optimize the Mean Squared Error (MSE) as the fitness function during the training process of the WNN model, obtaining the optimal fitness value corresponding to the network parameters. Then, the obtained parameters are used as the network model parameters of WNN for wind speed prediction. To validate the effectiveness of the proposed method, the model is validated using the measured wind speed data from the Chuanshan Port Area of Ningbo-Zhoushan Port throughout 2022, and its performance is compared with three other models: SSA–BP, SSA–LSTM, and WNN. The results demonstrate that the proposed prediction model exhibits good performance in port wind speed prediction and outperforms the other comparative models in terms of prediction accuracy and convergence speed. [ABSTRACT FROM AUTHOR]

In coastal cities, a large amount of cold energy is released into the nearby seawater during the process of regasification of liquefied natural gas (LNG) at the receiving terminal, and meanwhile, a large amount of exported and/or imported foods need to be frozen by bulk cold energy near the sea. In this paper, a new multistage cold energy recovery/utilization system is investigated to link the LNG cold energy directly to supply the coastal cold store. This design uses a realistic case in Ningbo LNG receiving station in China, and for the very first time considers how to utilize the LNG cold energy in an efficient manner regarding the technical, economic, and environmental benefits. A three‐stage LNG cold energy utilization system is designed and evaluated by considering different temperatures, pressures, and heat exchange parameters. Compared to conventional electric‐driven cold stores, the proposed multistage cold energy recovery/utilization system has a much cheaper capital cost during the initial investment stage, and also avoids the utility power consumption during the daily operation period. In the case study, from Ningbo LNG receiving station to a 10 MW class cold store, the proposed multienergy system can save the energy of 20.05 kilotons of standard coal, and the carbon emission reduction is up to 1.49 × 108 kg per year. [ABSTRACT FROM AUTHOR]

The built environment of cities has increasingly affected the travel mode of residents and led to changes in energy consumption, which is crucial to maintaining urban sustainability. Ningbo is a typical representative of urbanization on the east coast of China, and its energy consumption is in a period of rapid growth. Therefore, using the survey data of 22,112 traffic trip samples from nine streets in Ningbo, this paper establishes a regression analysis model, systematically analyzes the relationship between the built environment and domestic energy consumption from multiple dimensions, and reveals the impact mechanism of the built environment on domestic energy consumption. We find that (1) social and economic conditions are the main factors affecting traffic energy consumption. (2) The population density has a significant negative correlation effect on the energy consumption of transportation trips. When the population density increases by 1%, the energy consumption of total transportation trips, commuting trips, high-energy-consumption trips, and low-energy-consumption trips decreases by 0.094%, 0.115%, 0.273%, and 0.124%, respectively. (3) When the degree of mixed use of land increases by one percentage point, the energy consumption of total transportation trips, commuting trips, high-energy-consumption trips, and low-energy-consumption trips decreases by 0.415%, 0.421%, 2.574%, and 1.197%, respectively. (4) The density of road intersections has a significant negative correlation effect on the energy consumption of traffic trips. (5) The impact of the built environment on the energy consumption of transportation trips is greater than that of residential buildings. [ABSTRACT FROM AUTHOR]

Accurate prediction of air-conditioning energy consumption in buildings is of great help in reducing building energy consumption. Nowadays, most research efforts on predictive models are based on large samples, while short-term prediction with one-month or less-than-one-month training sets receives less attention due to data uncertainty and unavailability for application in practice. This paper takes a government office building in Ningbo as a case study. The hourly HVAC system energy consumption is obtained through the Ningbo Building Energy Consumption Monitoring Platform, and the meteorological data are obtained from the meteorological station of Ningbo city. This study utilizes a Gaussian process regression with the help of a 12 × 12 grid search and prediction processing to predict short-term hourly building HVAC system energy consumption by using meteorological variables and short-term building HVAC energy consumption data. The accuracy R2 of the optimal Gaussian process regression model obtained is 0.9917 and 0.9863, and the CV-RMSE is 0.1035 and 0.1278, respectively, for model testing and short-term HVAC system energy consumption prediction. For short-term HVAC system energy consumption, the NMBE is 0.0575, which is more accurate than the standard of ASHRAE, indicating that it can be applied in practical energy predictions. [ABSTRACT FROM AUTHOR]

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The premise of implementing the total amount control plan of building carbon emission should be based on the prediction of the future change trend of building carbon emission. This paper investigates and analyzes peaks of building carbon emission in Ningbo city of China through the Ningbo's building energy monitoring platform and uses the STIRPAT model to predict the medium and long-term carbon emissions in Ningbo. The peak time and peak value of carbon in the construction field at the city level under the dynamic scenario are simulated based on the Monte Carlo method. The results show that under the benchmark scenario, the carbon emission in the construction field of Ningbo will peak around 2037, with a peak of 22.68 million tons. Through the improvement of building energy-saving laws and regulations, the popularization of green energy-saving buildings, and the establishment of a building energy-saving system, the Ningbo construction department will achieve its carbon emission peak time in 2031 (±2), with a peak of about 15.45 (±1.07) million tons of CO2. [ABSTRACT FROM AUTHOR]

In this work, based on the concept of collaborative water–energy development, a multi-scenario system dynamics simulation model of a water–energy coupling system was constructed by using the system dynamics modeling method. The model was composed of four subsystems: society, economic, water resources, and energy. Taking Ningbo City as the research location to run the simulation model, the analysis of the validity of the model showed that the relative error between the historical data and the simulation results of the model was less than 10%, which proved that the model passed the test. In this paper, based on the scenario of business an usual (BAU), three scenarios of water-saving scenario (WSS), energy-saving scenario (ESS), and comprehensive savings (CS, the comprehensive scenario considers water-saving and energy-saving together) were designed, and the simulation indexes in the three scenarios were refined in order to strengthen the control of water-saving policies, improve the effective use of water, optimize the industrial energy structure, improve the level of energy-saving-related technologies, and advance the urbanization process. The simulation results for Ningbo City from 2010 to 2030 show that the water–energy coupling system is affected by many factors, and the adjustment of a driving factor of any subsystem will have an impact on the water–energy coupling system. There are two driving factors: the first is a constant variable related to water resources, energy, society, and economic, and the second is a variable affected by time. The coupling system is based on the law of real development and is composed of causal and functional relationships between variables. Therefore, within the prediction range of 2030, the driving factors in the coupling system are controllable, and there is no uncontrollable situation. The strengthening of water-saving policies and the improvement of the coefficient of the effective utilization of water will have the optimal saving effects on water resources and energy at both the single and the coupling level; this also demonstrates that the water resource management in Ningbo City plays an extremely important role in the relationship of the water–energy coupling. The results of this study are expected to provide a valuable reference for the management and conservation of water–energy coupling in Ningbo City. [ABSTRACT FROM AUTHOR]

This study developed a method to determine the optimum supply of shared cars according to personal travel behaviors in the condition of minimum energy consumption. We applied the method in Southern Higher Education Zone (SHEZ) of Ningbo based on the survey data of personal travel behaviors in the region. Solutions in the case of minimum energy consumption encourage more shared-car trips, less private-car trips, and mass-transit trips. Total of 111 kJ of energy could be saved by each student from the actual case to minimum energy case. In SHEZ, 76 shared cars are recommended to provide an optimal supply of shared cars compared to 87 shared cars in reality. According to the findings, two policy implications are put forward from the aspects of supply and demand. First, to make an efficient supply, companies should focus on the distribution of shared cars according to the temporal and spatial distribution of usage, rather than only number of vehicles. Meanwhile, it is important to increase the usage of shared cars through policies, such as campaigns that raise residents' awareness of car sharing as a green, flexible, and all-distance travel option for both workdays and holidays, and countermeasures that promote the usage of shared cars by key groups such as non-car owners. This study not only provides a method to estimate the optimum supply from viewpoint of minimum energy consumption, but also provide policy implications to guide the behaviors of both companies and users to improve the efficient usage of shared cars in Ningbo real case. [ABSTRACT FROM AUTHOR]