scholarly journals Analysis of Influencing Factors of Heat Load of Heating Power Station

2021 ◽  
Vol 252 ◽  
pp. 01042
Author(s):  
Yu Lu ◽  
Rui Li
Keyword(s):  
Influencing Factors ◽  
Heat Load ◽  
Load Forecasting ◽  
Heating System ◽  
Heating Power ◽  
Power Station ◽  
System Planning ◽  
Factors Affecting ◽  
Heating Load ◽  
Selection Of

Heating system load forecasting is very important in heating system planning and formulating heating plans. More accurate load forecasting can obtain greater economic and social benefits for the heating power station. The selection of influencing factors affecting heating load forecasting has a decisive effect on the accuracy of load forecasting. Based on the analysis of heat load of typical heating power station, this paper compares and analyses the influence of outdoor temperature, wind speed and load of previous days, etc. The main factors that should be considered in heating load forecasting are obtained.

A Study of Predictive Control of Energy-Saving of Heating Process

2011 ◽  
Vol 328-330 ◽  
pp. 1810-1813
Author(s):  
Xue Li Zhu ◽  
Shu Xian Zhu ◽  
Sheng Hui Guo
Keyword(s):  
Control System ◽  
Error Correction ◽  
Energy Saving ◽  
Real Time ◽  
Predictive Control ◽  
Control Method ◽  
Heating System ◽  
Heating Power ◽  
Heating Process ◽  
Power Station

This paper presents a predictive control method of heating system of heating power station. Firstly, the forecast of heating load is introduced using time series analysis, and the obtained result is used as an energy-saving initial value of predictive control system. Secondly, model simplification method is given and immediate control law is derived, the predictive model order is decreased from N to n. Simplification model satisfies the demand of real-time property of the control system. Thirdly, predictive error correction is used to replace error correction to implement the correction of optimum control of the system, which can improve adaptability and robustness of the system. Finally, simulation of heating system of heating power station is conducted and the results prove that the algorithm is effective in ensuring real-time control, improving tracking and robustness property.

scholarly journals Research on Influencing Factor Selection of Pilot’s Intention

2020 ◽  
Vol 2020 ◽  
pp. 1-13
Author(s):  
Haibo Wang ◽  
Ting Pan ◽  
Haiqing Si ◽  
Yao Li ◽  
Naiqi Jiang
Keyword(s):  
Influencing Factors ◽  
Flight Control ◽  
Influencing Factor ◽  
Physical Parameters ◽  
Cognitive State ◽  
Factors Affecting ◽  
Active Safety System ◽  
Factor Sequence ◽  
Automatic Pilot ◽  
Selection Of

The physiological, psychological, and physical characteristics of the pilot will have an impact on flight safety, mainly in the pilot’s intention. In another word, this means the pilot’s psychological experience of flight status under the influence of various factors and the preference for decision-making or behavioral value that is displayed. The pilot’s intention is to reflect the cognitive state that the pilot showed during the maneuvering of the aircraft. The exploration of intention is very important for the study of automatic pilot and flight control active safety system. Also, it is an important concept often involved in the study of human factors in flight, especially the microbehavior of pilots. Pilot’s intention is taken as the study object in this paper; physiological-psychological-physical parameters are obtained through analyzing their influencing factors from the simulating flight experiments designed. The random forest analysis method is used to rank the main influencing factors affecting the pilot’s intention, and the factor sequence is formed. The results provide a good foundation for further research on the pilot’s intention identification.

scholarly journals ANN and ANFIS for Short Term Load Forecasting

2018 ◽  
Vol 8 (2) ◽  
pp. 2818-2820
Author(s):  
J. Chakravorty ◽  
S. Shah ◽  
H. N. Nagraja
Keyword(s):  
Artificial Intelligence ◽  
Power System ◽  
Influencing Factors ◽  
Electrical Engineering ◽  
Load Forecasting ◽  
Economic Load Dispatch ◽  
Mathematical Methods ◽  
Short Term ◽  
System Planning ◽  
Short Term Load Forecasting

Load forecasting has become one of the major areas of research in electrical engineering. Short term load forecasting (STLF) is essential for power system planning and economic load dispatch. A variety of mathematical methods has been developed for load forecasting. This paper discusses the influencing factors of STLF and an artificial intelligence (AI) based STLF model for MGVCL load. It also includes comparison of various AI models. Our main objective is to develop the best suited model for MGVCL, by critically evaluating the ways in which the AI techniques proposed are designed and tested.

Selection of factors affecting increase of non-oil export

2018 ◽  
Vol 13 (1) ◽  
pp. 45-52
Author(s):  
Aynura Manaf kizi Ismayilova ◽  
Keyword(s):  
Factors Affecting ◽  
Oil Export ◽  
Selection Of

Regional, institutional and individual factors affecting selection of minimally invasive nephroureterectomy in Japan: A national database analysis

2012 ◽  
Vol 20 (7) ◽  
pp. 695-700 ◽  
Author(s):  
Toru Sugihara ◽  
Hideo Yasunaga ◽  
Hiromasa Horiguchi ◽  
Tetsuya Fujimura ◽  
Hiroaki Nishimatsu ◽  
...  
Keyword(s):  
Minimally Invasive ◽  
National Database ◽  
Individual Factors ◽  
Database Analysis ◽  
Factors Affecting ◽  
Selection Of

scholarly journals Aphid–Plant–Phytovirus Pathosystems: Influencing Factors from Vector Behaviour to Virus Spread

Agriculture ◽  
2021 ◽  
Vol 11 (6) ◽  
pp. 502
Author(s):  
Junior Corneille Fingu-Mabola ◽  
Frédéric Francis
Keyword(s):  
Influencing Factors ◽  
Virus Transmission ◽  
Physical Factors ◽  
Factors Affecting ◽  
Vector Borne Diseases ◽  
Vector Behaviour ◽  
Crop Fields ◽  
Host Seeking ◽  
The Impact ◽  
Vector Efficiency

Aphids are responsible for the spread of more than half of the known phytovirus species. Virus transmission within the plant–aphid–phytovirus pathosystem depends on vector mobility which allows the aphid to reach its host plant and on vector efficiency in terms of ability to transmit phytoviruses. However, several other factors can influence the phytoviruses transmission process and have significant epidemiological consequences. In this review, we aimed to analyse the aphid behaviours and influencing factors affecting phytovirus spread. We discussed the impact of vector host-seeking and dispersal behaviours mostly involved in aphid-born phytovirus spread but also the effect of feeding behaviours and life history traits involved in plant–aphid–phytovirus relationships on vector performances. We also noted that these behaviours are influenced by factors inherent to the interactions between pathosystem components (mode of transmission of phytoviruses, vector efficiency, plant resistance, …) and several biological, biochemical, chemical or physical factors related to the environment of these pathosystem components, most of them being manipulated as means to control vector-borne diseases in the crop fields.

scholarly journals The role of serum β-trophin and endostatin in patients with polycystic ovary syndrome: Are they correlated?

2021 ◽  
Vol 21 (1) ◽  
Author(s):  
Wei Gong ◽  
Aikmu Bilixzi ◽  
Xinmei Wang ◽  
Yanli Lu ◽  
Li Wan ◽  
...  
Keyword(s):  
Polycystic Ovary Syndrome ◽  
Influencing Factors ◽  
Polycystic Ovary ◽  
Pearson Correlation ◽  
Healthy Controls ◽  
Newly Diagnosed ◽  
Ovary Syndrome ◽  
Factors Affecting ◽  
Main Factors

Abstract Background It’s necessary to investigate the serum β-trophin and endostatin (ES) level and its influencing factors in patients with newly diagnosed polycystic ovary syndrome (PCOS). Methods Newly diagnosed PCOS patients treated in our hospital were selected, and healthy women who took physical examination during the same period as healthy controls. We detected and compared the related serum indicators between two groups, Pearson correlation were conducted to identify the factors associated with β-trophin and ES, and the influencing factors of β-trophin and ES were analyzed by logistic regression. Results A total of 62 PCOS patients and 65 healthy controls were included. The BMI, WHI, LH, FSH, TT, FAI, FBG, FINS, HOMA-IR, TC, TG, LDL, ES in PCOS patients were significantly higher than that of healthy controls, while the SHBG and HDL in PCOS patients were significantly lower than that of healthy controls (all p < 0.05). β-trophin was closely associated with BMI (r = 0.427), WHR (r = 0.504), FBG (r = 0.385), TG (r = 0.405) and LDL (r = 0.302, all p < 0.05), and ES was closely associated with BMI (r = 0.358), WHR (r = 0.421), FBG (r = 0.343), TC (r = 0.319), TG (r = 0.404, all p < 0.05). TG, BMI, WHR and FBG were the main factors affecting the serum β-trophin levels (all p < 0.05). FBG, TC and BMI were the main factors affecting the serum ES levels (all p < 0.05). The TG, β-trophin, ES level in PCOS patients with insulin resistance (IR) were significantly higher than that of those without IR (all p < 0.05). Conclusion Increased β-trophin is closely associated with increased ES in patients with PCOS, which may be the useful indicators for the management of PCOS.

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