scholarly journals Forecasting of Heat Production in Combined Heat and Power Plants Using Generalized Additive Models

Energies ◽  
2021 ◽  
Vol 14 (8) ◽  
pp. 2331
Author(s):  
Maciej Bujalski ◽  
Paweł Madejski
Keyword(s):  
Heat Production ◽  
Heat Load ◽  
Combined Heat And Power ◽  
Additive Models ◽  
Measuring System ◽  
Weather Data ◽  
Data Set ◽  
District Heating System ◽  
Input Variables ◽  
The Impact

The paper presents a developed methodology of short-term forecasting for heat production in combined heat and power (CHP) plants using a big data-driven model. An accurate prediction of an hourly heat load in the day-ahead horizon allows a better planning and optimization of energy and heat production by cogeneration units. The method of training and testing the predictive model with the use of generalized additive model (GAM) was developed and presented. The weather data as an input variables of the model were discussed to show the impact of weather conditions on the quality of predicted heat load. The new approach focuses on an application of the moving window with the learning data set from the last several days in order to adaptively train the model. The influence of the training window size on the accuracy of forecasts was evaluated. Different versions of the model, depending on the set of input variables and GAM parameters were compared. The results presented in the paper were obtained using a data coming from the real district heating system of a European city. The accuracy of the methods during the different periods of heating season was performed by comparing heat demand forecasts with actual values, coming from a measuring system located in the case study CHP plant. As a result, a model with an averaged percentage error for the analyzed period (November–March) of less than 7% was obtained.

scholarly journals Weather Impact on Airport Performance

Aerospace ◽  
2018 ◽  
Vol 5 (4) ◽  
pp. 109 ◽  
Author(s):  
Michael Schultz ◽  
Sandro Lorenz ◽  
Reinhard Schmitz ◽  
Luis Delgado
Keyword(s):  
Traffic Management ◽  
Probability Distributions ◽  
Weather Conditions ◽  
Air Traffic ◽  
Weather Data ◽  
Data Set ◽  
Airport Capacity ◽  
Weather Events ◽  
The Individual ◽  
The Impact

Weather events have a significant impact on airport performance and cause delayed operations if the airport capacity is constrained. We provide quantification of the individual airport performance with regards to an aggregated weather-performance metric. Specific weather phenomena are categorized by the air traffic management airport performance weather algorithm, which aims to quantify weather conditions at airports based on aviation routine meteorological reports. Our results are computed from a data set of 20.5 million European flights of 2013 and local weather data. A methodology is presented to evaluate the impact of weather events on the airport performance and to select the appropriate threshold for significant weather conditions. To provide an efficient method to capture the impact of weather, we modelled departing and arrival delays with probability distributions, which depend on airport size and meteorological impacts. These derived airport performance scores could be used in comprehensive air traffic network simulations to evaluate the network impact caused by weather induced local performance deterioration.

scholarly journals Abundance patterns (1984-1987 / 1994-1998) of polychaete worms (Annelida) from an estuarine tidal flat, Pacific, Costa Rica

2015 ◽  
Vol 7 (2) ◽  
pp. 233-248
Author(s):  
José A. Vargas-Zamora ◽  
Jeffrey A. Sobaja-Cordero ◽  
Harlan K. Dean ◽  
Sylvia Solano-Upate
Keyword(s):  
Costa Rica ◽  
Anthropogenic Activities ◽  
Enso Event ◽  
Additive Models ◽  
Intertidal Flat ◽  
Red Tides ◽  
Data Set ◽  
Abundance Patterns ◽  
The Impact

The objectives of this report are to provide an updated list of the annelid polychaete worm species found at a tropical estuarine intertidal flat, describe long term oscillations of 11 of the species, and the impact of red tides as evidenced by PCA. From 1984 to 1987 (49 dates) 14 sediment cores (17.7 cm2 – 15 cm deep) were collected per date at low tide from a 400 m2 muddy-sand plot in the Gulf of Nicoya estuary (10oN-85oW), Pacific, Costa Rica. All cores were fixed in Rose Bengal stained formalin and sieved thru a 500 micron mesh screen. A total of 43 species of polychaetes were found and distributed among 25 families and 6600 individuals, of which 80% were represented by: Mediomastus californiensis (32.4%), Caraziella calafia (20.3%), Paraprionospio alata (9.2%), Scolotema tetraura (5.9%), Gymnonereis crosslandi (4.9%), Spiophanex duplex (3.8%), and Glycinde armigera (3.5%). M. californiensis was numerically dominant during most of the sampling dates. The Spionidae (6), Phyllodocidae (4), and Nereididae (3) were the more speciose polychaete families. Populations of all species were patchy in space and time. The abundance patterns of 11 species are illustrated for the 1984-1987 data set. These patterns may reflect declining populations at the beginning of 1984 perhaps influenced by the strong 1982-1983 ENSO event. During 1985 red tides may have influenced the abundances of polychaetes as indicated by the results of a PCA. This is the first time that population patterns of nine species of intertidal polychaetes over a three year period, and the impact of red tides on these worms are reported for this region of the eastern Pacific. General Additive Models (GAM) were applied to the abundances of M. californiensis and P. alata found during 1984-1987 and to additional data from 1994 to 1996 (28 dates) The GAM approach confirmed ealier observations of seasonal oscillations of these species during 1984-1987, but these trends were not found during 1994-1998. Previously unnoticed underlying patterns of unknown origin were also detected by the application of GAM. The theoretical framework needed for the interpretation of results from tropical benthic surveys could improve significantly from more long term monitoring. Long term abundance data is essential to evaluate the impacts of anthropogenic activities in estuaries.

How flood risk impacts residential rents and property prices

2018 ◽  
Vol 36 (1) ◽  
pp. 50-67 ◽  
Author(s):  
Jens Hirsch ◽  
Jonas Hahn
Keyword(s):  
Flood Risk ◽  
Generalized Additive Models ◽  
Additive Models ◽  
Data Set ◽  
Content Type ◽  
Real Estate Markets ◽  
Property Price ◽  
The Usa ◽  
Rental Markets ◽  
The Impact

Purpose The purpose of this paper is to quantify the impact of 100-year flood risk on both property rents and values in Germany, exemplified by the market of the historic city of Regensburg, and therefore supports investors in understanding market behavior patterns in both rental and investment context. Design/methodology/approach The authors construct two generalized additive models for rents and purchasing prices with spatial components and under inclusion of both typical property characteristics (as control variables) and a 100-year flood risk parameter in order to estimate its effect on the rents and property price structure. The authors apply the methodology to a four-year data set of more than 16,500 observations. Findings The analysis shows that flood risk is a highly significant parameter when estimating both the rent as well as the sales price model. The authors also find that purchase prices for one square meter of living area are, on average, EUR299 lower if the property is located in the flood risk zone. In addition, also rental markets come with a respective, but rather low, discount. Practical implications The authors provide transparency to investors in terms of the impact that a flood risk location has on property rents as well as purchasing prices. The study supports investors by providing evidence on reaction patterns in German real estate markets and helps quantifying the financial impact that comes with flood risk in Germany. Originality/value This is the first study that aims to empirically test and to quantify the impact of flood risk on property rents and purchasing prices in Germany. Related research has been performed for the USA, Ireland and New Zealand and largely refers to event-driven work or rather conceptual in the context of property valuation.

scholarly journals Analysis of the Impact of Decreasing District Heating Supply Temperature on Combined Heat and Power Plant Operation

2014 ◽  
Vol 14 (1) ◽  
pp. 41-46 ◽  
Author(s):  
Alona Bolonina ◽  
Genadijs Bolonins ◽  
Dagnija Blumberga
Keyword(s):  
Power Plant ◽  
District Heating ◽  
Heating System ◽  
Combined Heat And Power ◽  
District Heating System ◽  
Heating Systems ◽  
Plant Operation ◽  
Operation Modes ◽  
Power Plant Operation ◽  
The Impact

Abstract District heating systems are widely used to supply heat to different groups of heat consumers. The district heating system offers great opportunities for combined heat and power production. In this paper decreasing district heating supply temperature is analysed in the context of combined heat and power plant operation. A mathematical model of a CHP plant is developed using both empirical and theoretical equations. The model is used for analysis of modified CHP plant operation modes with reduced district heating supply temperature. Conclusions on the benefits of new operation modes are introduced.

Impact of weather conditions on airport arrival delay and throughput

2021 ◽  
Vol ahead-of-print (ahead-of-print) ◽  
Author(s):  
Álvaro Rodríguez-Sanz ◽  
Javier Cano ◽  
Beatriz Rubio Fernández
Keyword(s):  
Decision Making ◽  
Weather Conditions ◽  
Weather Data ◽  
Arrival Process ◽  
Data Set ◽  
Content Type ◽  
Weather Events ◽  
Adverse Weather ◽  
Arrival Processes ◽  
The Impact

Purpose Weather events have a significant impact on airport arrival performance and may cause delays in operations and/or constraints in airport capacity. In Europe, almost half of all regulated airport traffic delay is due to adverse weather conditions. Moreover, the closer airports operate to their maximum capacity, the more severe is the impact of a capacity loss due to external events such as weather. Various weather uncertainties occurring during airport operations can significantly delay some arrival processes and cause network-wide effects on the overall air traffic management (ATM) system. Quantifying the impact of weather is, therefore, a key feature to improve the decision-making process that enhances airport performance. It would allow airport operators to identify the relevant weather information needed, and help them decide on the appropriate actions to mitigate the consequences of adverse weather events. Therefore, this research aims to understand and quantify the impact of weather conditions on airport arrival processes, so it can be properly predicted and managed. Design/methodology/approach This study presents a methodology to evaluate the impact of adverse weather events on airport arrival performance (delay and throughput) and to define operational thresholds for significant weather conditions. This study uses a Bayesian Network approach to relate weather data from meteorological reports and airport arrival performance data with scheduled and actual movements, as well as arrival delays. This allows us to understand the relationships between weather phenomena and their impacts on arrival delay and throughput. The proposed model also provides us with the values of the explanatory variables (weather events) that lead to certain operational thresholds in the target variables (arrival delay and throughput). This study then presents a quantification of the airport performance with regard to an aggregated weather-performance metric. Specific weather phenomena are categorized through a synthetic index, which aims to quantify weather conditions at a given airport, based on aviation routine meteorological reports. This helps us to manage uncertainty at airport arrival operations by relating index levels with airport performance results. Findings The results are computed from a data set of over 750,000 flights on a major European hub and from local weather data during the period 2015–2018. This study combines delay and capacity metrics at different airport operational stages for the arrival process (final approach, taxi-in and in-block). Therefore, the spatial boundary of this study is not only the airport but also its surrounding airspace, to take both the arrival sequencing and metering area and potential holding patterns into consideration. Originality/value This study introduces a new approach for modeling causal relationships between airport arrival performance indicators and meteorological events, which can be used to quantify the impact of weather in airport arrival conditions, predict the evolution of airport operational scenarios and support airport decision-making processes.

scholarly journals Development of an AI Model to Measure Traffic Air Pollution from Multisensor and Weather Data

Sensors ◽  
2019 ◽  
Vol 19 (22) ◽  
pp. 4941 ◽  
Author(s):  
Hai-Bang Ly ◽  
Lu Minh Le ◽  
Luong Van Phi ◽  
Viet-Hung Phan ◽  
Van Quan Tran ◽  
...  
Keyword(s):  
Air Pollution ◽  
Absolute Humidity ◽  
Absolute Error ◽  
Weather Data ◽  
Weather Variables ◽  
The Mean ◽  
Input Variables ◽  
Transport Emissions ◽  
The Impact ◽  
Quality Indexes

Gas multisensor devices offer an effective approach to monitor air pollution, which has become a pandemic in many cities, especially because of transport emissions. To be reliable, properly trained models need to be developed that combine output from sensors with weather data; however, many factors can affect the accuracy of the models. The main objective of this study was to explore the impact of several input variables in training different air quality indexes using fuzzy logic combined with two metaheuristic optimizations: simulated annealing (SA) and particle swarm optimization (PSO). In this work, the concentrations of NO2 and CO were predicted using five resistivities from multisensor devices and three weather variables (temperature, relative humidity, and absolute humidity). In order to validate the results, several measures were calculated, including the correlation coefficient and the mean absolute error. Overall, PSO was found to perform the best. Finally, input resistivities of NO2 and nonmetanic hydrocarbons (NMHC) were found to be the most sensitive to predict concentrations of NO2 and CO.

scholarly journals Weather Variables Associated with Spore Dispersal of Lecanosticta acicola Causing Pine Needle Blight in Northern Spain

Plants ◽  
2021 ◽  
Vol 10 (12) ◽  
pp. 2788
Author(s):  
Nebai Mesanza ◽  
David García-García ◽  
Elena R. Raposo ◽  
Rosa Raposo ◽  
Maialen Iturbide ◽  
...  
Keyword(s):  
Additive Models ◽  
Brown Spot ◽  
Weather Data ◽  
Maximum Temperature ◽  
Daily Maximum Temperature ◽  
Spore Dispersal ◽  
Northern Spain ◽  
Lecanosticta Acicola ◽  
The Impact ◽  
Needle Blight

In the last decade, the impact of needle blight fungal pathogens on the health status of forests in northern Spain has marked a turning point in forest production systems based on Pinus radiata species. Dothistroma needle blight caused by Dothistroma septosporum and D. pini, and brown spot needle blight caused by Lecanosticta acicola, coexist in these ecosystems. There is a clear dominance of L. acicola with respect to the other two pathogens and evidence of sexual reproduction in the area. Understanding L. acicola spore dispersal dynamics within climatic determinants is necessary to establish more efficient management strategies to increase the sustainability of forest ecosystems. In this study, spore counts of 15 spore traps placed in Pinus ecosystems were recorded in 2019 and spore abundance dependency on weather data was analysed using generalised additive models. During the collection period, the model that best fit the number of trapped spores included the daily maximum temperature and daily cumulative precipitation, which was associated to higher spore counts. The presence of conidia was detected from January and maximum peaks of spore dispersal were generally observed from September to November.

scholarly journals Heat Load Numerical Prediction for District Heating System Operational Control

2021 ◽  
Vol 58 (3) ◽  
pp. 121-136
Author(s):  
D. Rusovs ◽  
L. Jakovleva ◽  
V. Zentins ◽  
K. Baltputnis
Keyword(s):  
Ambient Temperature ◽  
Heat Load ◽  
District Heating ◽  
Heating System ◽  
Training Data ◽  
Mean Bias Error ◽  
Percentage Error ◽  
Data Set ◽  
District Heating System ◽  
Heating Systems

Abstract To develop an advanced control of thermal energy supply for domestic heating, a number of new challenges need to be solved, such as the emerging need to plan operation in accordance with an energy market-based environment. However, to move towards this goal, it is necessary to develop forecasting tools for short- and long-term planning, taking into account data about the operation of existing heating systems. The paper considers the real operational parameters of five different heating networks in Latvia over a period of five years. The application of regression analysis for heating load dependency on ambient temperature results in the formulation of normalized slope for the regression curves of the studied systems. The value of this parameter, the normalized slope, allows describing the performance of particular heating systems. Moreover, a heat load forecasting approach is presented by an application of multiple regression methods. This short-term (day-ahead) forecasting tool is tested on data from a relatively small district heating system with an average load of 20 MW at ambient temperature of 0 °C. The deviations of the actual heat load demand from the one forecasted with various training data set sizes and polynomial orders are evaluated for two testing periods in January of 2018. Forecast accuracy is assessed by two parameters – mean absolute percentage error and normalized mean bias error.

Age at Exposure to Parental Suicide and the Subsequent Risk of Suicide in Young People

Crisis ◽  
2018 ◽  
Vol 39 (1) ◽  
pp. 27-36 ◽  
Author(s):  
Kuan-Ying Lee ◽  
Chung-Yi Li ◽  
Kun-Chia Chang ◽  
Tsung-Hsueh Lu ◽  
Ying-Yeh Chen
Keyword(s):  
Young People ◽  
Proportional Hazards ◽  
Proportional Hazards Model ◽  
Cox Proportional Hazards ◽  
Cox Proportional Hazards Model ◽  
Birth Registry ◽  
Data Set ◽  
Parental Suicide ◽  
The Impact ◽  
Age At Exposure

Abstract. Background: We investigated the age at exposure to parental suicide and the risk of subsequent suicide completion in young people. The impact of parental and offspring sex was also examined. Method: Using a cohort study design, we linked Taiwan's Birth Registry (1978–1997) with Taiwan's Death Registry (1985–2009) and identified 40,249 children who had experienced maternal suicide (n = 14,431), paternal suicide (n = 26,887), or the suicide of both parents (n = 281). Each exposed child was matched to 10 children of the same sex and birth year whose parents were still alive. This yielded a total of 398,081 children for our non-exposed cohort. A Cox proportional hazards model was used to compare the suicide risk of the exposed and non-exposed groups. Results: Compared with the non-exposed group, offspring who were exposed to parental suicide were 3.91 times (95% confidence interval [CI] = 3.10–4.92 more likely to die by suicide after adjusting for baseline characteristics. The risk of suicide seemed to be lower in older male offspring (HR = 3.94, 95% CI = 2.57–6.06), but higher in older female offspring (HR = 5.30, 95% CI = 3.05–9.22). Stratified analyses based on parental sex revealed similar patterns as the combined analysis. Limitations: As only register-­based data were used, we were not able to explore the impact of variables not contained in the data set, such as the role of mental illness. Conclusion: Our findings suggest a prominent elevation in the risk of suicide among offspring who lost their parents to suicide. The risk elevation differed according to the sex of the afflicted offspring as well as to their age at exposure.

State Medical Boards' Perceptions of a Minimum Data Set and Current Practices for Collecting Physician Information

2013 ◽  
Vol 99 (4) ◽  
pp. 40-45 ◽  
Author(s):  
Aaron Young ◽  
Philip Davignon ◽  
Margaret B. Hansen ◽  
Mark A. Eggen
Keyword(s):  
Media Coverage ◽  
The United States ◽  
Minimum Data Set ◽  
Direct Patient Care ◽  
Physician Workforce ◽  
Data Set ◽  
Minimum Data ◽  
State Medical Boards ◽  
The Impact ◽  
Current Practices

ABSTRACT Recent media coverage has focused on the supply of physicians in the United States, especially with the impact of a growing physician shortage and the Affordable Care Act. State medical boards and other entities maintain data on physician licensure and discipline, as well as some biographical data describing their physician populations. However, there are gaps of workforce information in these sources. The Federation of State Medical Boards' (FSMB) Census of Licensed Physicians and the AMA Masterfile, for example, offer valuable information, but they provide a limited picture of the physician workforce. Furthermore, they are unable to shed light on some of the nuances in physician availability, such as how much time physicians spend providing direct patient care. In response to these gaps, policymakers and regulators have in recent years discussed the creation of a physician minimum data set (MDS), which would be gathered periodically and would provide key physician workforce information. While proponents of an MDS believe it would provide benefits to a variety of stakeholders, an effort has not been attempted to determine whether state medical boards think it is important to collect physician workforce data and if they currently collect workforce information from licensed physicians. To learn more, the FSMB sent surveys to the executive directors at state medical boards to determine their perceptions of collecting workforce data and current practices regarding their collection of such data. The purpose of this article is to convey results from this effort. Survey findings indicate that the vast majority of boards view physician workforce information as valuable in the determination of health care needs within their state, and that various boards are already collecting some data elements. Analysis of the data confirms the potential benefits of a physician minimum data set (MDS) and why state medical boards are in a unique position to collect MDS information from physicians.

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