Data centers (DCs) uninterruptedly run 24/24 h, 365 days per year with much huge operating scale, and have the characteristics of high operation safety requirement, high heat flux density, high ...energy consumption and high carbon emission. They are influential energy consumers and carbon emitters in building or even global energy sectors (around 3% of global energy consumption), who are also significant waste heat producer (e.g., waste heat from year-round uninterrupted operation of IT equipment and cooling system). Huge energy consumption has increased the burden on the global energy industry, while carbon and direct waste heat emissions have also caused great damages to the outdoor environment. Thus, it is critical to improve the energy efficiency in DCs and to realize the energy conservations and environmental deterioration alleviation. Waste heat recovery technology is considered as a promising approach to improve energy efficiency, achieve energy and energy cost savings, and mitigate environmental impacts (caused by both carbon emission and waste heat discharge) at the same time. This article conducts a comprehensive review on recovering waste heat from all kind of sources (e.g., exhaust air, circulating water, and coolants) in DCs for various energy uses (e.g., heating supply, district heating supplement, cooling and electricity productions, and industrial/agricultural production process) and different application scenarios (e.g., office buildings, comprehensive energy community and residential buildings), while the future research and development proposals for DC waste heat recoveries are given through technical, energy, environmental and economic analysis.
•Conduct a comprehensive review on waste heat recoveries in data centers•Consider various waste heat sources (exhaust air, circulating water, and coolants)•Regard various waste heat uses (heating, DH, cooling and power generation, etc.)•Regard various applications (office, residential buildings, and energy community)•Give recommendations for future study through technical and 3 E analysis
This paper presents an ensemble algorithm based on a new load decomposition method to forecast short-term metropolitan-scale electric load. In this method, a decision tree for hourly seasonal ...attributes and a weighted average method for daily seasonal attributes are first applied to divide seasons into a completely different way. Then, the load of transition seasons is chosen as a basic component according to power load characteristics, and the differences between total load and the basic component are extracted as the weather-sensitive component. Finally, a time-series method is selected to forecast the basic component and SVM (Support Vector Machine) to the weather-sensitive component. This paper takes the annual electricity load of Shanghai as a case study to verify this ensemble method. The results show that compared with the traditional model based on overall daily load and other load decomposition methods—EMD (Empirical Mode Decomposition) and WT (Wavelet Transform), this ensemble model reduces the error from 3 to 5% to lower than 2% when forecasting the power load of workdays, and for non-work days, the error is decreased from 4 to 5% to lower than 4%.
•A control strategy is proposed to use HVAC systems for frequency regulation service.•The strategy ensures the service quality when large regulation capacity is provided.•The proposed strategy is a ...machine learning-based strategy that has good robustness.
Heating, ventilation and air-conditioning systems (HVAC), at demand side, have been regarded increasingly as promising candidates to provide frequency regulation service to smart power grids. In many control systems, chilled water outlet temperature setpoint is reset to change the power use of HVAC systems after the regulation capacity is determined. However, the conflict between changed power use and unchanged cooling/heating demand could become a prominent problem when a large regulation capacity is provided. This problem can deteriorate the performance of frequency regulation service provided by HVAC systems. In this study, a machine learning-based control strategy is proposed to solve this problem for improved performance of HVAC systems in providing large capacity of frequency regulation service. It adjusts the power use of HVAC systems by simultaneously resetting chilled water outlet temperature setpoint and indoor temperature setpoint. The proposed control strategy is validated on a simulation platform. Results show that the strategy can significantly increase the performance of service when an HVAC system provides different regulation capacities. Moreover, the robustness of the strategy is studied. The results show that the strategy can still work effectively even the machine learning algorithms has a relatively low prediction performance in real application due to practical difficulties.
•A novel scheduling approach is proposed for residential building DR programs.•A nondominated sorting genetic algorithm II (NSGA-II) is applied for multi-objective optimization.•Peak load reduction, ...occupant's comfort levels, and capital benefits are optimized.•Household appliances are promising flexible loads by rescheduling work time.
In recent years, with the rapid growth of electricity demand and the development of smart grids, demand-side management had an important role. As the penetration rate of distributed renewable energy generation in the grid increases, the diurnal peak of the net load demand curve in the urban area is offset by renewable energy sources such as photovoltaics and the peak load time gradually shifts from afternoon to evening. The peak electricity load of residential buildings usually occurs in the evening, which aggravates the power balance problem. To this end, this study proposes a demand response (DR) scheduling approach for residential buildings, aimed for four types of residential building loads: interruptible and deferrable loads, noninterruptible and deferrable loads, noninterruptible and nondeferrable loads, and air conditioning loads. Nondominated sorting genetic algorithm II is used as a multi-objective optimization algorithm to search for the minimal electricity cost and minimal inconvenience index. Finally, the ASHRAE 140 standard building is used as a case and the proposed scheduling approach is evaluated under two scenarios of working and nonworking days. The proposed scheduling approach can effectively shave the peak load to off-peak load time, reduce electricity bills, and meet the occupants’ comfort.
•Energy performance and energy flexibility of a novel system were investigated.•An average heating COP of 1.33 is achieved for five representative load cases.•This system achieves considerable ...electricity flexibility for a residential building.•This novel system can provide 2 h of heating supply without electricity access.
The energy performance and energy flexibility potential of a sorption-assisted water storage (SAWS) for a medium-scale residential building with eight apartments under the climatic condition of Potsdam, Germany were investigated. The SAWS system consists of a stratified water storage coupled to an adsorption heat pump (AHP). The driving heat source for the AHP was modeled as an ideal heater, which can be interpreted as an electric heater to serve as an off-peak storage heating system. Five representative partial heating loads of a building were investigated. Recharging situations of 2 h, 4 h, and 8 h with an interval of 2 h heater switching off were considered. Transient simulation results show that the 2 h heater switch off is feasible and does not significantly reduce the occupants’ thermal comfort in the four low heating load cases. However, for the highest heating load case, the load cannot be completely met. An average heating coefficient of performance of 1.33 for all the five load cases is achieved. Additionally, under the simple control scheme considered here, this SAWS system achieves high electricity flexibility by controlling the heater switch on and off schedule for a demand response program.
There are many problems with the present centralized energy systems. With the advancement of distributed energy systems and the advancement of renewable energy technologies, the supply of electricity ...and heat will be gradually decentralized, forming the Energy Internet combined with information technology that makes energy transactions easier and more efficient. In this research, a bidding mechanism of peer to peer electricity system is proposed for supplier nodes and consumer nodes to sell or buy energy on the market. And computer simulation is applied to test and optimize the mechanism. The results indicate that the proposed mechanism conforms to the market regularity and can improve the efficiency of local energy utilization compared with the traditional centralized system, reduce the cost of consumers, propel the utilization of renewable energy, and make the market more flexible and transparent.
Demand response is an efficient method to flatten the demand curves of end-use customers. This article studies the feasibility of an adsorption heat pump in the demand response of a residential ...building under winter operating conditions. Stratified storage is introduced into the stratified heat pump system to realize heat recovery, which is also used as a buffer energy source when power shortages occur. This article studies the performance of an adsorption heat pump when the system disconnects the external energy source to simulate the situation of a demand response event occurring through experiments. Moreover, the heating loads of a typical residential building are obtained from the simulations on EnergyPlus. Two day types are selected to evaluate the demand response performance of the system in different situations. The coolest day presents an extreme situation, and the design day represents a normal situation. The demand response potential of the adsorption heat pump is estimated by comparing the heating capacity of the system and heating load curves of the residential building.
Celotno besedilo
Dostopno za:
BFBNIB, DOBA, GIS, IJS, IZUM, KILJ, KISLJ, NUK, PILJ, PNG, SAZU, UILJ, UKNU, UL, UM, UPUK
Nasopharyngeal carcinoma (NPC) is a major head and neck cancer with high occurrence in Southeast Asia and southern China. Insulin receptor substrate
(
) plays an important role in the development, ...progression, invasion and metastasis of tumors. The purpose of this study was to evaluate whether
could be used as biomarkers for the diagnosis of NPC through measuring their expression and assess their relationship with clinical pathological factors.
Quantitative real-time reverse transcriptase-polymerase chain reaction (qRT-PCR) and Western blot were used to analyze the expression of
in 133 NPC patients and 104 healthy controls. The relationship between
expression and clinicopathological characteristics in NPC was estimated through chi-square test. We calculated diagnostic values of serum
expression by receiver operating characteristic (ROC) curve.
This study reports that IRS-1 protein was weakly expressed in NPC specimens, but highly in healthy controls. Serum
were up-regulation in NPC patients compared with healthy controls. Their up-regulation was significantly correlated with lymph node status (
=0.029). Furthermore, the value of the area under the receiver-operating characteristic curve (AUC-ROC) was 0.907. The optimal cutoff value was 2.255, providing a sensitivity of 88.0% and a specificity of 77.9% in differentiating NPC patients from healthy controls.
Our data indicates that serum
might increase the sensitivity and accuracy in diagnosis of NPC, and may be a potential target for diagnosis and gene therapy.
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