Search

Physics reports 894, 1-116 (2021). doi:10.1016/j.physrep.2020.09.003 special issue: "The Alpha Magnetic Spectrometer (AMS) on the International Space Station: Part II : Results from the First Seven Years", Published by Elsevier Science, North-Holland, Amsterdam [u.a.]

Physical review letters 126(4), 041104 (2021). doi:10.1103/PhysRevLett.126.041104, Published by APS, College Park, Md.

International audience; Precise knowledge of the charge and rigidity dependence of the secondary cosmic ray fluxes and the secondary-to-primary flux ratios is essential in the understanding of cosmic ray propagation. We report the properties of heavy secondary cosmic ray fluorine F in the rigidity $R$ range 2.15 GV to 2.9 TV based on 0.29 million events collected by the Alpha Magnetic Spectrometer experiment on the International Space Station. The fluorine spectrum deviates from a single power law above 200 GV. The heavier secondary-to-primary F/Si flux ratio rigidity dependence is distinctly different from the lighter B/O (or B/C) rigidity dependence. In particular, above 10 GV, the $^{\textrm{F/Si}}_{\textrm{B/O}}$ ratio can be described by a power law $R^δ$ with $δ=0.052±0.007$. This shows that the propagation properties of heavy cosmic rays, from F to Si, are different from those of light cosmic rays, from He to O, and that the secondary cosmic rays have two classes.

As building digitalization is accelerating, one of the challenges is making intelligent use of building energy consumption data. Since appliance-level energy consumption data is not generally available, Non-Intrusive Load Monitoring (NILM) provides novel ways to disaggregate total energy consumption into appliance-level data while ensuring the privacy of customers. This work focuses on a NILM algorithm that is applicable to common appliances and widespread smart metering infrastructure. The energy consumption data of an office was collected with a 1-min resolution and used for analysis. A fuzzy c-means clustering NILM algorithm and inter-cluster entropy were used to classify data and verify different categories of office appliances. The algorithm was proven to be able to disaggregate and classify office appliance energy consumption data with a satisfactory accuracy.

Intra-city Public Charging Stations (PCSs) play a crucial role in promoting the mass deployment of Electric Vehicles (EVs). To motivate the investment on PCSs, this work proposes a novel framework to find the optimal location and size of PCSs, which can maximize the benefit of the investment. The impacts of charging behaviors and urban land uses on the income of PCSs are taken into account. An agent-based trip chain model is used to represent the travel and charging patterns of EV owners. A cell-based geographic partition method based on Geographic Information System is employed to reflect the influence of land use on the dynamic and stochastic nature of EV charging behaviors. Based on the distributed charging demand, the optimal location and size of PCSs are determined by mixed-integer linear programming. Vasteras, a Swedish city, is used as a case study to demonstrate the model's effectiveness. It is found that the charging demand served by a PCS is critical to its profitability, which is greatly affected by the charging behavior of drivers, the location and the service range of PCS. Moreover, charging price is another significant factor impacting profitability, and consequently the competitiveness of slow and fast PCSs.

The main scientific goal of the LHAASO WFCTA experiment is to measure the cosmic ray energy spectra and composition from 10 TeV to 1 EeV. Cherenkov photons in the extensive air shower measured by the SiPM camera of Cherenkov telescopes can be used to reconstruct the cosmic ray energy. The absolute calibration of the camera is a crucial step to achieve the accurate measurement of the cosmic ray energy spectrum. A multi-wavelength cylindrical illuminator based on LEDs is developed and mounted inside the telescope to calibrate and monitor the camera, and the illuminator’s stability is better than 0.5% under the temperature variation from -26 to 26 ° C. A portable probe with a single photoelectron resolution of 21.6% is developed. After calibration by National Institute of Metrology, China (NIM), the probe is taken to the LHAASO site to measure the absolute photon density of the cylindrical illuminator inside the telescope. Based on the illuminator with known photon density, the photon conversion factor of the camera can be calibrated, and the overall calibration uncertainty is less than 2.6%.

In this paper, a multi-agent system (MAS) was developed to simulate the operation of an energy hub (EH) with different penetration rates (PRs) and various charging patterns of electric vehicle (EV) ...

In order to optimize the configuration and operation of multi-energy complementary distributed energy systems, based on the existing optimization methods, this paper proposes an improved comprehensive evaluation model: the improved model comprehensively evaluates energy-saving, economic and environmental performance of the system, and factors including off-design condition performance and start-stop performance have also been considered. After the model is established, a certain virtual building is optimized based on the genetic algorithm, and equipment capacity and hourly equipment operating load are obtained. By comparing the improved model proposed with the simplified model, it can be found that the results obtained from the simplified one cannot meet the actual needs of the system and is unreasonable, thus validating the rationality of the improved model.

The prevailing High-rise Residential Buildings (HRBs) alleviate land shortage in modern cities, but energy sustainability in these energy-intensive constructions remains a big problem. The large available surface area of HRB makes rooftop attached PV technique a competent solution to utilize the local renewable energy. In the future, this can be expanded through facade installations. However, to which degree could PV generation support the HRB electricity consumption remains unclear. With more variations coming from renewable energy sources to the grid, peak-load electricity reduction will become critical. In this paper, the peak load shaving potential of rooftop attached PVs in HRB is estimated. Multi-agent system framework is employed to identify the building and occupant characteristics of HRB electricity consumption. A series of comparisons between demand and net demand profiles of HRB in a typical summer week are conducted. The results show that PV could effectively reduce the peak load during daytime, however, the maximum PV generation in the midday may cause new valleys in the net demand profiles. The results suggest that energy storage should be used to optimally match demand and supply in order to smoothen the net demand profiles.

This paper aims to explore the charging demand of electric vehicles (EVs) in multi-locations. To this aim, a model, which involves 4 charging locations: residential, work, business and recreation area, and 7 trip purposes: end trip, home linger, work, shopping, meal, pick / drop and public recreation, were developed to study the charging of EVs using agent-based trip chain method. The driving patterns of EVs are modelled based on 2017 NHTS data and then validated. The charging profile of 4 locations under uncontrolled charging are obtained, the difference caused by locations and supply equipment (normal charging and rapid charging) are analyzed. The rapid charging profile of 2 public locations and the relationship between normal and rapid charging are investigated. Finally, suggestions on charging stations planning in different area are provided after the analysis.

Based on the general regression (GR) and back propagation (BP) neural network prediction method, this work forecasts the generated power of photovoltaic (PV) power station. First, the Pearson correlation coefficient method was used to analyze the meteorological factors. The degree of correlation between complex weather factors and PV power output was differentiated and the irradiance and battery temperature were selected as the important influencing variables. Second, the weather was classified according to the certain classification criteria. Then, we established the model by using GR and BP neural network prediction methods. The relative errors were within acceptable limits. The former model is more convenient while the latter model has better nonlinear fitting capacity. The results of the two models are compared and analyzed. We find out that the BP neural prediction method have better prediction results than GR method on PV power generation. Our findings can not only provide valuable information for the optimal dispatching of micro-grid and photovoltaic power, but also be of great significance in energy management and hierarchical control of micro-grid.

This study proposes a micro-combined heat and power (CHP) system by combining a proton exchange membrane fuel cell (PEMFC) and a heat pump (HP) to simultaneously meet the thermal and electrical demand in a residential area. The main components of the system are: the fuel processing unit, fuel cell (FC) unit and HP unit. In the proposed scenario the heat produced by the reforming process and FC is exploited by feeding both the radiant floor heating system and the domestic hot water (DHW). A zero-dimensional model was established in ASPEN PLUS to simulate the HP-integrated PEMFC-based micro-CHP system. An exergy and economic analysis has been performed to evaluate the plant feasibility. In traditional fuel cell systems for residential application, the installation is usually made of a small FC system able to cover the basic electrical load and a large boiler to cover the heating and DHW demand. To evaluate the performance of the proposed system better, we set the boiler-integrated PEMFC-based micro-CHP system as a reference. The results proved the superiority of this system over conventional technologies as regards meeting a high thermal load in the residential sector.

Due to the intermittency and uncertainty in photovoltaic (PV) power outputs, not only deterministic point predictions (DPPs), but also associated prediction Intervals (PIs) are important information for promoting the application of PV in practice, especially when grid connection continues to grow. While there are few studies focused on quantifying the uncertainty of forecasting PV power outputs, this paper developed a novel two-stage model to quantify the PIs of PV power outputs. In the first stage, three different neural networks, namely Generalized Regression Neural Network (GRNN), Extreme Learning Machine Neural Network (ELMNN) and Elman Neural Network (ElmanNN), were integrated using the Genetic Algorithms optimized Back Propagation (GA-BP) method to develop a Weight-Varying Combination Forecast Mode (WVCFM) model. The WVCFM model was applied to generate DPPs. In the second stage, a nonparametric kernel density estimation (NKDE) method was adopted to estimate the PIs regarding the statistical distribution of the errors of the DPPs derived in the first stage. The proposed method was tested using four types of PV output and weather data measured from a 15 kW grid-connected PV system. The cover percentage of prediction intervals (PICPs) were computed under the confidence level of 95%, 90%, 85% and 80%, respectively. The results imply that the two-stage model proposed in the paper outperforms conventional forecast methods in terms of prediction of short-term PV power outputs and associated uncertainties.

Growing concerns about energy conservation and the environmental impacts of greenhouse gas emissions over the world have promoted the development of the electric vehicles (EVs) market. However, one ...

The idea that electric vehicles can connect to grid to serve as energy storage energy devices is compelling, especially in situations where traditional forms of storage, back-up energy supply is unavailable or expensive, or considering the volatility of frequency. So far, the viability and reliability of vehicle-to-grid(V2G) has become a very popular research topic. Previous studies mainly focused on the maximum potential of V2G capacity, which, however, is usually unrealistic to achieve. The potential capacity of V2G is relevant to the size of batteries and the penetration rates of electric vehicle. This article developed an agent-based model to estimate the real amount of electricity that vehicles can discharge to the grid. Three battery sizes under the penetration rate of 90% and the penetration rate from 10% to 90% were investigated. The results show that the increase of battery capacity led to an increase of V2G potential capacity from 1650.2 kW to 1868.5 kW and that increase from 10% to 90% in penetration rate could account for V2G potential capacity change from 162.8 kW to 1650.2 kW.

Solar thermal heaters (STs), photovoltaics (PVs) and ground source heat pumps (GSHPs) are widely used nowadays due to their advantages of environmental protection and energy saving. The combination of solar thermal, photovoltaic and GSHP can achieve an even better performance and such a system is referred to as PV/ST-GSHP in this study. The overall efficiency of the PV/ST-GSHP system is highly dependent on the capacity size which is large or small would affect the overall efficiency and the power of PVs and STs affects the efficiency as well. This paper established an optimization model for a PV/ST-GSHP system based on the environment, energy and economy criteria. The model focused on the optimal planning of PV and ST under the restriction of a limited size of area and of the practical loads of heat and cooling, for which the rated capacity of GSHP is included. Genetic algorithm is employed to solve the optimization problem. After optimization, the multi-objective value is 26.88%.

The combination of hydropower and PV power could make full use of the advantages of water and solar energy, which forms the integrated hydro-PV system. In this paper, a short-term optimization approach for integrated hydro-PV system is proposed, which involves the prediction model of PV power and GA-based multi-objective optimization model of the integrated system. To this aim, the short-term PV power output under three types of weather is firstly predicted by hourly solar radiation and module temperature using the nonlinear multiple regression (NMR) model. The objective functions with respect to maximum power output of the integrated system and minimum variation between complementary and original power, and the constraints are derived according to the calculating principles of hydropower. Based on the optimization model consisting of objective functions and constraints, genetic algorithm (GA) method is applied in the modelling of the optimization problem to obtain the optimal solution of the operation and scheduling of the integrated system. The optimal solutions showed that the integrated system could achieve good complementarity between hydropower and PV in time scale using the GA optimization method.

Uncertainty is a common and critical problem for planning the capacity and operation of integrated energy systems (IESs). This study evaluates the effects of uncertainties on the capacity and operation of an IES. To this aim, system planning and operation with uncertainties are optimized by a two-stage stochastic programming model and compared with a referencing deterministic case. Specifically, the uncertainties of photovoltaic (PV) generation and energy demand are investigated. Regarding system capacity, a larger energy storage capacity is needed to accommodate a higher uncertainty. The superimposed uncertainties have a higher effect on system capacity than the sum of the effect of each uncertainty. The uncertainty of energy demand has a higher impact than the uncertainty of PV generation. Regarding system operation, the increase in operation cost is smaller than the increase in investment cost and total cost. In addition, the average flexibility provided by the energy storage increases with uncertainty and uncertainties affect the change rate for power charging/discharging of the electric energy storage. Regarding the effect on the grid, the uncertainties increase not only the magnitude of ramping-rate, but also the frequency of power-dispatch.

This paper proposed a novel approach to optimize the operation of an energy hub based on a multi-agent based model which includes hub element agents, a multi-energy demands agent, a commercial agent, an integrated dispatch agent and an environment agent. The novel optimization architecture is a combination and evolution of day-ahead and hour-ahead management method based on agents’ pre-defined roles and their interactions. The off-design condition performance and start-stop loss of hub units are considered in the model. A simple case study is taken to enforce the optimization architecture. The results validated the efficient operation of the developed system, proving its ability to optimize the operation of energy hub.

Climate Change is known to have a great impact on the energy consumption in buildings. This paper studied the cooling load of a residential building in Jinan, China, in 2020, 2050, 2080 by considering the impacts of climate change on the influential parameters for cooling loads. The Hadley Centre Coupled Model 3(HadCM3) was employed to investigate the impact of climate change on the outdoor temperature, solar radiation and wind speed. In addition, this study also differentiated the cooling loads according to occupants’ age, since the energy usage patterns and the requirements on cooling loads are closely related to occupants’ age. The results suggest that climate change will create significant impacts on the cooling loads in residential buildings. Regarding occupants’ age, the impact on the occupants under 55-year-old is larger than the people older than 55.

The characteristics of intermittent and stochastic of solar energy has brought great challenges to power grid system in terms of operation and regulation. Power forecasting is an important factor for optimal schedule of power grid system and assessing the working performance of PV systems. In order to forecast the power output of a PV system located in Ashland at 24-hour-ahead for higher efficiency, a back propagation (BP) neural network model is proposed. Before designing the model, correlation analysis is done to investigate the relationship between power output and solar irradiance and ambient temperature, which are key parameters affecting the power output of PV systems. Based on a correlation analysis, the model admitted the following input parameters: hourly solar radiation intensity, the highest, the lowest daily and the average daily temperature, and hourly power output of the PV system. The output of the model is the forecasted PV power output 24 hours ahead. Based on the datasets, the neural network is trained to improve its accuracy. The best performance is obtained with the BP neural network structure of 28-20-11. The analysis of the error indicator MAPE shows that the proposed model has great accuracy and efficiency for forecasting the power output of photovoltaic systems.

This paper developed an agent-based model (ABM) to explore the energy saving potentials (ESPs) of various types of appliances in offices under different pricing mechanisms. The model included four types of commonly used appliances in office buildings: an air conditioner (AC), computers, lights and a basic load. The total ESPs of the entire office are 6.7% and 17.4% on the second and the third price tier of the tiered pricing mechanism (TEP), while the ESPs are 11.8% and 14.2% under the peak-valley pricing (PVP) and critical peak pricing (CPP), respectively. Within different types of appliances, AC consumes the largest amount of electricity, over 50%, while the ESPs of the AC under different pricing mechanisms are only 6.9–12.1%. In contrast, the lights have the biggest ESP, i.e. 14.1–53.4%, under various pricing levels. Both the pricing mechanisms of PVP and CPP only have the effect of peak clipping and do not have a significant effect of valley filling, since there is no people working in the office during the valley price period. The maximum ESP, which is based on people’s maximum-saving behavior, is much larger than the ESPs on the basis of people’s ordinary consumption patterns. This implies the importance of improving people’s awareness of energy saving and refining their behaviors. Lastly, the model developed in this study provides a generic platform for simulating many types of energy systems and is very effective for handling the complicated relations between different types of technology and the way how they are used and interacted with each other. ABMs have very good adaptability and capacity in simulating energy systems.

Based on life cycle assessment (LCA), this paper compares five different methods for food waste treatment. To achieve it, the environmental performance of food waste in Shandong University as a case study is evaluated. The case study indicates that saving food is the ultimate way to reduce environment impact and the distance for transportation also affects a lot. The result of comparison shows that landfill contributes most to climate change, which is about 10 times larger than others. For acidification and eutrophication, incineration shows the worst result 7.77 PAF*m2yr. And composting has the largest impact on carcinogens with the result 3.49E-05 DALY. On the whole, the waste treatment technologies are recommended in proper sequence from anaerobic digestion, heat-moisture reaction, composting and incineration to landfill.

Technology of phase change materials used in cold storage house has great potential in energy storage and cost saving under the background of peak and valley price of electricity. A kind of cold st ...

An agent-based model is developed to estimate the potential of energy savings of an office room under different pricing mechanisms. For this aim, an energy balance model was developed to simulate the operation of air conditioner operation, the heat flux of different behavior and the electricity consumption of different types of appliances according different energy consumption patterns was investigated in detail. The total electricity consumption reduced 3.9%-17.1% under different pricing mechanisms. Light's energy saving potential is the biggest among the electric appliances. The maximum-saving behavior is investigated and the total energy saving potential of it is 26.91%.

The development of heat metering has promoted the development of statistic models for the prediction of heat demand, due to the large amount of available data, or big data. Weather data have been commonly used as input in such statistic models. In order to understand the impacts of direct solar radiance and wind speed on the model performance comprehensively, a model based on Elman neural networks (ENN) was adopted, of which the results can help heat producers to optimize their production and thus mitigate costs. Compared with the measured heat demand, the introduction of wind speed and direct solar radiation has opposite impacts on the performance of ENN and the inclusion of wind speed can improve the prediction accuracy of ENN. However, ENN cannot benefit from the introduction of both wind speed and direct solar radiation simultaneously.

Conventional approaches for modelling energy consumption for data center are limited to deal with the randomness, emergence and interdependence in energy systems. To fill the gap, an agent-based method was proposed and implemented to simulate the electricity consumption of a data center and to explore the potential of energy saving. The study found that a large amount of energy can be saved by dynamically operating the cooling load according to the practical load of the servers, namely 13% of the total energy consumption can be saved in the dynamic energy saving case (DESC) and 17% in the enhanced dynamic energy saving case (EDESC). The power usage effectiveness (PUE) of the data center can decrease from 1.71 in the original situation to 1.49 in the DESC and 1.42 in the EDESC. By contrast, the frequency of data backup has a heavy impact on the electricity consumption of the data center, while the frequency of computing tasks do not.

With the rapid development of E-Commerce, the express industry is also undergoing unprecedented growth. Research on the express industry in terms of consumption of materials and energy can help the government to develop policies and regulations for the long-term development of the industry and help people to improve their awareness of energy conservation and environment protection. The study used life cycle assessment (LCA) approach to examine the environmental impacts and energy consumption of delivery packages used for express purposes, which mainly include corrugated boxes, plastic bags and tapes for binding and sealing purposes. The results showed that production and usage stage consume most energy and create most environmental impacts. 1-layer box use less materials and energy and thus can replace 2-layer box to reduce environmental impacts. Recycling is the best method for handling waste packages among various treatment methods.

There is more or less consensus around the problems related to the existing energy systems. Most focus has been on the negative environmental effects of using fossil fuels. Many papers and reports conclude that renewable energy has the potential to run the world and the technology needed to do so is available. An important question is: why such a big potential is only marginally utilized? This paper analyzes the reasons why we ended up in today’s energy systems. In order to develop pathways for change, scenarios can be used to support decision making involving all key actors in the society. The scenarios must be established on the comparison between different primary energy sources at a fundamental thermodynamic level. The paper proposes the principles for evolutionary forecasting scenarios (EFS) with two sets of criteria to investigate technology development and long-term energy transition. Primary criteria are based on the first and second law of thermodynamics. They can be used to simulate the development of energy transformation technologies. The result implies that engineers must consider the system level when they develop energy technology. The secondary criteria, which are mainly based on subjective-values, should be used for evaluating different roadmaps obtained from these simulations. It is important that the evaluations of scenarios are carried out in broad dialogue processes with key actors in order to make the roadmaps easy to implement and follow up in practice.

Energy storage system is an important unit to maintain stable output of the system, reduce energy cost, and realize peak-shaving in the integrated energy system. For the two commonly used energy storage methods, electrical energy storage and thermal energy storage, coordinating the optimal configuration of the two can make the system more efficient and energy-saving operation. At present, uncertainty brings great complexity to the optimal design of the system. At the same time, ignoring the influence of uncertainty on the system will lead to the optimization result being a suboptimal situation. Thus, in this paper, a multiobjective optimization under uncertainty is carried out to find the optimal configuration of electric and thermal energy storage system.

Over the recent decades, there has been an explosive growth in China’s urban buildings and related energy consumption. Great efforts have been done to transform the urban buildings into more sustainable paradigms. The aim of this work is to better understand the ongoing energy transition in China. Through a research based on qualitative data analysis, this work provides insights into the transition of China’s urban buildings towards deep decarbonization, decentralization and digitalization. This work also furnishes a discussion of the energy system configuration of the transitioning urban buildings.

To increase the microgrid’s (MG) profit and decrease the MG’s power loss, the cooperation between MGs has been studied. But there is lack of a specific mechanism for determining how to distribute alliance’s total profit to different MGs and an impact on balancing market’s cost. Hence, this paper firstly confirmed the electricity market mechanism. Then, established a model of direct transaction between MGs and main grid (DTMM) and proposed a model of cooperative transaction between MGs (CTM) based on interbank lending method. Finally, the paper used chronological simulation method to simulate the models proposed on MATLAB. Results showed that all MGs’ profit increased by 359160 € and the balancing market’s cost decreased by 91% in CTM compared with DTMM.

Understanding the volatility dynamics of electricity markets is important in evaluating the deregulation experience and in pricing electricity futures. This paper introduced an approach to portray the volatility of electricity price. We first introduced the Integrated Volatility (IV) and Quadratic Variation (QR) theory to define the volatility. Based on the theories, two nonparametric methods, i.e. Classical Realized Volatility (RV) and Realized Bi-power Volatility (RBV) were adopted to respectively measure the electricity price volatility with half-hour resolution in High-Frequency (HF) environment, using historical data from Victoria, Australia. By comparing the characterizing performance, the RBV method exhibited more superior effect. Next, the paper separated the total volatility into jump and continuous components based on Z jump test and the distinct dynamics of the Jump Volatility (JV) and Continuous Volatility (CV) were also analyzed.

The idea that electric vehicles (EVs) can connect to the grid to serve as energy storage energy devices is compelling, especially in situations where traditional forms of storage, back-up energy supply is unavailable or expensive, or considering the volatility of frequency. So far, the viability and reliability of vehicle-grid integration, which represents smart charging (SC) and vehicle to grid (V2G) of electric vehicles, has become a very popular research topic. Previous studies mainly focused on the maximum potential of SC and V2G capacity on account of too many assumptions, which, however, is usually unrealistic to achieve. The charging and V2G flexibility of EVs are estimated from both adjustable power and adjustable amount aspects with model derived from real-world data. Overall, the EVs at home locations have the largest charging and V2G flexibility under the uncontrolled charging (UC) strategy, only in the regular working time, the maximum adjustable charging power at home location is smaller than that at work locations. Meanwhile, the V2G flexibility, is generally larger than charging flexibility at work and public locations.

Solar thermal heaters (STs), photovoltaics (PVs) and ground source heat pumps (GSHPs) are widely used nowadays due to their advantages of environmental protection and energy saving. The combination of solar thermal, photovoltaic and GSHP can achieve an even better performance and such a system is referred to as PV/ST-GSHP in this study. However, renewable energy resources, such as solar energy, have an intermittent nature, and thus it is necessary to use an energy storage system. This paper aimed to have a comparison between different forms of energy storage in a PV/STGSHP system. The capacity of the energy storage in four different systems was optimized by a multi-objective approach, which concerns economic, energy and environmental objectives. The results showed that energy storage can improve the overall performance of the system. In the different forms of energy storage, the integration of thermal and electrical energy storage performed better than other forms of energy storage.

Electric Vehicles (EVs) have achieved a significant development because of the continuous technology revolution and policy supports in recent years, which leads to a larger demand of EV charging stations (EVCSs). Strategies about optimal siting and sizing of public EVCSs are urgently needed in order to further assist the development of EVs. This paper focus on the return of investments on EVCSs and proposes a Mixed Integer Linear Programming (MILP) model based on Geographic Information System (GIS) to identify the optimal location and size of EVCS in cities. Traffic flow data, aggregated charging profiles and land-use classifications are used as important inputs together with important constraints, are included in the MILP model with the objective function of maximizing the total profits of new charging stations. The effectiveness of the proposed method is then demonstrated by implementing a case study in Vasteras, Sweden.

A BP neural network combined with the Adam optimization algorithm and the Mini-batches Learning algorithm was established for predicting the temperature of BOX-C on Alpha Magnetic Spectrometer (AMS) in this paper. After training, the Mean Squared Error (MSE) of the prediction results under the normal operating condition is 0.14 and this shows that the model can be used to predict the temperature of BOX-C with a satisfying accuracy. The model paves the ground for AMS thermal control on orbit.

In a typical cement plant, the cement clinker production consumes 80% of the total energy used in cement production. The grate plate is the main sector of the grate cooler, and its structure can be modified to receive electric power while cooling the clinker. In this paper, Ansys Fluent was used to simulate the flow field of an existing high-resistance grate plate. The structure of the existing high-resistance grate plate was improved based on the velocity and pressure fields. Results showed an improvement in the performance of the modified high-resistance grate plate–greater velocity and lower pressure drop were observed. When the pressure drop decreased by 51.58%, 2.84% of the total energy consumption could be saved. These results, to some extent, guided the rational design of high-resistance grate plates, and played an indirect role in energy conservation as well.

In this paper, we developed an agent-based model to simulate the electricity consumption in an office building under a tiered pricing mechanism. In the model, electricity users’ behavior is significantly influenced by the electricity price. The price and the range of each tier have great impacts on daily and total consumption, these parameters are discussed by sensitivity analysis. The result was compared with the consumption under single pricing. We found the daily consumption reduced more than 20% under the third tier price, the price change's influence has huge difference among different electric appliances.