scholarly journals Study of the forecasting problem of energy consumption of water pumping station

2019 ◽  
Vol 102 ◽  
pp. 03001
Author(s):  
Aleksandr V. Alekseev
Keyword(s):  
Energy Consumption ◽  
Moving Average ◽  
Real Data ◽  
Future Research ◽  
Wholesale Market ◽  
Pumping Station ◽  
Water Pumping ◽  
Object Properties ◽  
Forecasting Methods ◽  
City Water

The article describes the study of the energy consumption forecasting of city water pumping station. The review of the existing approaches for technical systems energy consumption forecasting is made. The shot description of the studied object properties including hourly energy consumption is presented. Two often used forecasting methods exponential smoothing and the autoregression of the integrated moving average methods was tested on real data. The results of predict calculations shows that the autoregression of the integrated moving average methods is suitable for energy consumption planning and can be used to submit an hourly bid for the required amount of the electricity in the wholesale market. Directions for future research is also presented.

scholarly journals Optimization of Energy Consumption in the Pumping Station Supplying Two Zones of the Water Supply System

Energies ◽  
2022 ◽  
Vol 15 (1) ◽  
pp. 310
Author(s):  
Martyna Świętochowska ◽  
Izabela Bartkowska
Keyword(s):  
Energy Consumption ◽  
Water Supply ◽  
Energy Optimization ◽  
Field Tests ◽  
Supply System ◽  
Water Supply System ◽  
Pumping Station ◽  
Water Pumping ◽  
System Pressure ◽  
Pumping Stations

Water supply pumping stations are among the main energy-consuming elements in the water supply system. The energy optimization of a pumping station can significantly affect the energy consumption of a water utility. This article deals with the energy optimization of water pumping stations. The work assumes several variants of optimization of water supply pumping stations through changes in the water supply system, pressure changes in the pumping station, and modification of the number of pumps. After analyzing the network, conducting field tests, and creating a model of the water supply network, the network was calibrated in order to reproduce the existing water network as accurately as possible. Then, a variant analysis was performed, and the best optimization method for the pumping station was selected. In two variants, there was a decrease in electricity consumption; in three there, was an increase; in one, there was no change. By connecting the DMA zones and modifying the pressure in the pumping station, the energy consumption of the pumping stations was reduced. On this basis, it was found that it is possible to optimize the water pumping station by modifying the pumping station and work related to the network layout.

scholarly journals Regime-Switching Discrete ARMA Models for Categorical Time Series

Entropy ◽  
2020 ◽  
Vol 22 (4) ◽  
pp. 458
Author(s):  
Christian H. Weiß
Keyword(s):  
Time Series ◽  
Regime Switching ◽  
Moving Average ◽  
Model Fitting ◽  
Real Data ◽  
Future Research ◽  
Arma Models ◽  
Categorical Time Series ◽  
Ordinal Time Series ◽  
Stochastic Properties

For the modeling of categorical time series, both nominal or ordinal time series, an extension of the basic discrete autoregressive moving-average (ARMA) models is proposed. It uses an observation-driven regime-switching mechanism, leading to the family of RS-DARMA models. After having discussed the stochastic properties of RS-DARMA models in general, we focus on the particular case of the first-order RS-DAR model. This RS-DAR ( 1 ) model constitutes a parsimoniously parameterized type of Markov chain, which has an easy-to-interpret data-generating mechanism and may also handle negative forms of serial dependence. Approaches for model fitting are elaborated on, and they are illustrated by two real-data examples: the modeling of a nominal sequence from biology, and of an ordinal time series regarding cloudiness. For future research, one might use the RS-DAR ( 1 ) model for constructing parsimonious advanced models, and one might adapt techniques for smoother regime transitions.

COVID-19: Time-Dependent Effective Reproduction Number and Sub-notification Effect Estimation Modeling (Preprint)

2020 ◽  
Author(s):  
Eduardo Atem De Carvalho ◽  
Rogerio Atem De Carvalho
Keyword(s):  
New York ◽  
Reproduction Number ◽  
Moving Average ◽  
Real Data ◽  
Time Dependent ◽  
Initial Value ◽  
Health Authorities ◽  
Reproduction Numbers ◽  
Effect Estimation ◽  
The Impact

BACKGROUND Since the beginning of the COVID-19 pandemic, researchers and health authorities have sought to identify the different parameters that govern their infection and death cycles, in order to be able to make better decisions. In particular, a series of reproduction number estimation models have been presented, with different practical results. OBJECTIVE This article aims to present an effective and efficient model for estimating the Reproduction Number and to discuss the impacts of sub-notification on these calculations. METHODS The concept of Moving Average Method with Initial value (MAMI) is used, as well as a model for Rt, the Reproduction Number, is derived from experimental data. The models are applied to real data and their performance is presented. RESULTS Analyses on Rt and sub-notification effects for Germany, Italy, Sweden, United Kingdom, South Korea, and the State of New York are presented to show the performance of the methods here introduced. CONCLUSIONS We show that, with relatively simple mathematical tools, it is possible to obtain reliable values for time-dependent, incubation period-independent Reproduction Numbers (Rt). We also demonstrate that the impact of sub-notification is relatively low, after the initial phase of the epidemic cycle has passed.

scholarly journals Effect of Roof Cooling and Air Curtain Gates on Thermal and Wind Conditions in Stadiums for Hot Climates

Energies ◽  
2021 ◽  
Vol 14 (13) ◽  
pp. 3941
Author(s):  
Fangliang Zhong ◽  
Hassam Nasarullah Chaudhry ◽  
John Kaiser Calautit
Keyword(s):  
Energy Consumption ◽  
Electric Energy ◽  
Building Energy ◽  
Future Research ◽  
World Cup ◽  
Air Curtain ◽  
Hot Air ◽  
Computational Fluid Dynamics Cfd ◽  
Energy Consumptions ◽  
The Spectator

To host the 2022 FIFA World Cup, Qatar is facing the greatest challenge in balancing the energy consumptions for cooling the stadiums and the thermal comfort for both players and spectators. Previous studies have not considered using a combined configuration of air curtain and roof cooling supply slot in stadiums to prevent the infiltration of outside hot air and reduce the cooling system’s energy consumption. This paper presents a Computational Fluid Dynamics (CFD) study of thermal and wind modeling around a baseline stadium and simulates the cooling scenarios of air curtains and roof cooling along with the energy consumption estimations for the World Cup matches using Building Energy Simulation (BES). Sensitivity analysis of different supply speeds and supply temperatures of air curtain gates and roof cooling was carried out, and the results showed that scenario six, which provides supply air of 25 m/s and 20 m/s at the roof and air curtain gates with a supply temperature of 10 °C, demonstrates optimal thermal performances on both the spectator tiers and the pitch. Compared with the baseline stadium performance, the average reductions in temperature on the pitch and spectator tiers under scenario six could reach 15 °C and 14.6 °C. The reductions in the Predicted Percentage of Dissatisfied values for the upper and lower tiers as well as the pitch were 63%, 74%, and 78%. In terms of the estimated energy consumptions, scenario six would consume electric energy per match at a rate of 25.5 MWh compared with 22.8 MWh for one of the stadiums in the 2010 South Africa World Cup and 42.0 MWh for the 2006 Germany World Cup. Future research is recommended to explore the influence of supply angle on air curtain gates and roof cooling supply slots’ performances.

scholarly journals Container Throughput Forecasting Using Dynamic Factor Analysis and ARIMAX Model

2017 ◽  
Vol 29 (5) ◽  
pp. 529-542 ◽  
Author(s):  
Marko Intihar ◽  
Tomaž Kramberger ◽  
Dejan Dragan
Keyword(s):  
Factor Analysis ◽  
Goodness Of Fit ◽  
Moving Average ◽  
Real Data ◽  
Information Criteria ◽  
Dynamic Factor Analysis ◽  
Forecasting Model ◽  
Dynamic Factor ◽  
Macroeconomic Indicators ◽  
The Impact

The paper examines the impact of integration of macroeconomic indicators on the accuracy of container throughput time series forecasting model. For this purpose, a Dynamic factor analysis and AutoRegressive Integrated Moving-Average model with eXogenous inputs (ARIMAX) are used. Both methodologies are integrated into a novel four-stage heuristic procedure. Firstly, dynamic factors are extracted from external macroeconomic indicators influencing the observed throughput. Secondly, the family of ARIMAX models of different orders is generated based on the derived factors. In the third stage, the diagnostic and goodness-of-fit testing is applied, which includes statistical criteria such as fit performance, information criteria, and parsimony. Finally, the best model is heuristically selected and tested on the real data of the Port of Koper. The results show that by applying macroeconomic indicators into the forecasting model, more accurate future throughput forecasts can be achieved. The model is also used to produce future forecasts for the next four years indicating a more oscillatory behaviour in (2018-2020). Hence, care must be taken concerning any bigger investment decisions initiated from the management side. It is believed that the proposed model might be a useful reinforcement of the existing forecasting module in the observed port.

A GAMMA MOVING AVERAGE PROCESS FOR MODELLING DEPENDENCE ACROSS DEVELOPMENT YEARS IN RUN-OFF TRIANGLES

2020 ◽  
pp. 1-22
Author(s):  
Luis E. Nieto-Barajas ◽  
Rodrigo S. Targino
Keyword(s):  
Latent Variables ◽  
Moving Average ◽  
Real Data ◽  
Model Parameters ◽  
Average Process ◽  
Moving Average Process ◽  
Data Set ◽  
Run Off ◽  
Average Form ◽  
Reserve Estimates

ABSTRACT We propose a stochastic model for claims reserving that captures dependence along development years within a single triangle. This dependence is based on a gamma process with a moving average form of order $p \ge 0$ which is achieved through the use of poisson latent variables. We carry out Bayesian inference on model parameters and borrow strength across several triangles, coming from different lines of businesses or companies, through the use of hierarchical priors. We carry out a simulation study as well as a real data analysis. Results show that reserve estimates, for the real data set studied, are more accurate with our gamma dependence model as compared to the benchmark over-dispersed poisson that assumes independence.

ICT as an enabler for sustainable development: reflections on opportunities and barriers

2015 ◽  
Vol 13 (1) ◽  
pp. 19-23 ◽  
Author(s):  
Richard Bull
Keyword(s):  
Energy Consumption ◽  
Digital Economy ◽  
Information And Communications Technology ◽  
First Century ◽  
Future Research ◽  
Daily Lives ◽  
Content Type ◽  
Comprehensive Knowledge ◽  
Future Challenges ◽  
Conceptual Paper

Purpose – Information and communications technology (ICT) offers a peculiar twenty-first century conundrum, as it offers both a cause and solution to rising carbon emissions. The growth in the digital economy is fueling increased energy consumption while affording new opportunities for reducing the environmental impacts of our daily lives. This paper responds and builds on Patrignani and Whitehouse’s overview of Slow Tech by providing examples of how ICT can be used to reduce energy. Encouraging examples are provided from the field of energy and buildings and implications for wider society are raised. Design/methodology/approach – This paper builds on the previous overview “The Clean Side of Slow Tech”, based on a comprehensive knowledge of literature of the latest developments in the field of digital economy, energy and sustainability. Findings – This paper provides clear and encouraging signs of how ICT can be used to contribute to sustainability through controlling systems more efficiently, facilitating behavioural changes and reducing energy consumption. Future challenges and recommendations for future research are presented. Originality/value – This conceptual paper presents the latest research into the use of ICT in energy reduction and offers cautious, but encouraging signs that while the environmental impact of ICT must not be overlooked, there are benefits to be had from the digital economy.

What every mathematician should know about modelling crime

2010 ◽  
Vol 21 (4-5) ◽  
pp. 275-281 ◽  
Author(s):  
MARCUS FELSON
Keyword(s):  
Mathematical Models ◽  
Real Data ◽  
Future Research ◽  
Data Modelling

This paper by a criminologist explains why it makes more sense to model criminal acts than to model criminals, how many preconceptions about crime can mislead modellers and offers some simple crime modelling ideas. Many opportunities for simulation now exist, and new opportunities for real-data modelling are emerging. The author suggests mathematical models of crime, including offender foraging for crime targets, as a rich area for future research.

Control by IoT in Drinking Water Pumping Station and Cisterns

2021 ◽  
Author(s):  
Ing. Leonardo Depetris ◽  
Ing. Damian Romani ◽  
Ing. Joaquin Gonzalez ◽  
Claudio Bottero ◽  
Maciel Rui ◽  
...  
Keyword(s):  
Drinking Water ◽  
Pumping Station ◽  
Water Pumping

Thermal performance assessment of cool roofs on supermarkets through case analysis in 13 cities

2021 ◽  
Vol ahead-of-print (ahead-of-print) ◽  
Author(s):  
Juan Carlos Ríos-Fernández
Keyword(s):  
Energy Consumption ◽  
Air Conditioning ◽  
Energy Savings ◽  
Real Data ◽  
Research Approach ◽  
Environmental Implications ◽  
Content Type ◽  
Cool Roof ◽  
Increase Energy Efficiency ◽  
Cool Roofs

PurposeThis paper aims to study the use of cool roof technology to avoid unnecessary energy consumption in supermarkets. This will allow to reduce and even cancel the heat absorbed by the roofs, transferring it to the buildings and thus, creating more sustainable cities.Design/methodology/approachThirteen real supermarkets with cool roofs were analysed in Australia, Canada, the USA and Spain. An analysis of so many supermarkets located in different parts of the world with different climatic zones has allowed an inductive analysis, obtaining real data of energy consumption associated with the air conditioning installations for a year with and without implementing the cool roof technology.FindingsThe paper provides insights on how the use of cool roof managed to reduce the need for energy for heating, ventilating and air conditioning by between 3.5 and 38%. Additionally, this technology reduces the annual generation of carbon dioxide (CO2) emissions per square meter of supermarket up to 2.7 kgCO2/m2. It could be an economical technology to apply in new and old buildings with a period of average economic recovery of four years.Research limitations/implicationsBecause of the chosen research approach, the research results may be generalisable. Therefore, researchers are encouraged to test proposals in construction with other uses.Practical implicationsThe paper includes economic and environmental implications for the development of cool roof technology and smooths the way for its implementation to increase energy efficiency in commercial buildings.Originality/valueThis paper is an innovative contribution to the application of cool roof technology as a source of energy savings in commercial construction through the analysis of supermarkets located in different countries with different climate zones. This will help other researchers to advance in this field and facilitate the implementation of the technology.

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