scholarly journals Enhancement of a Short-Term Forecasting Method Based on Clustering and kNN: Application to an Industrial Facility Powered by a Cogenerator

Energies ◽  
2019 ◽  
Vol 12 (23) ◽  
pp. 4407 ◽  
Author(s):  
Giulio Vialetto ◽  
Marco Noro
Keyword(s):  
Data Storage ◽  
Energy Demand ◽  
The Internet ◽  
Model Parameters ◽  
Industrial Facility ◽  
Short Term ◽  
K Nearest Neighbors ◽  
Industrial Facilities ◽  
Using Data ◽  
Short Term Forecasting

In recent years, collecting data is becoming easier and cheaper thanks to many improvements in information technology (IT). The connection of sensors to the internet is becoming cheaper and easier (for example, the internet of things, IOT), the cost of data storage and data processing is decreasing, meanwhile artificial intelligence and machine learning methods are under development and/or being introduced to create values using data. In this paper, a clustering approach for the short-term forecasting of energy demand in industrial facilities is presented. A model based on clustering and k-nearest neighbors (kNN) is proposed to analyze and forecast data, and the novelties on model parameters definition to improve its accuracy are presented. The model is then applied to an industrial facility (wood industry) with contemporaneous demand of electricity and heat. An analysis of the parameters and the results of the model is performed, showing a forecast of electricity demand with an error of 3%.

scholarly journals CORRELATION-REGRESSION ANALYSIS OF THE COMPETITIVENESS OF ENTERPRISE PRODUCTS

2020 ◽  
Vol 10 (86) ◽  
Author(s):  
Volodymyr Ulanchuk ◽  
◽  
Olena Zharun ◽  
Serhiy Sokolyuk ◽  
◽  
...  
Keyword(s):  
Mathematical Model ◽  
Regression Analysis ◽  
Correlation Equation ◽  
Regression Equation ◽  
Short Term ◽  
Flexible Tool ◽  
Proposed Model ◽  
Using Data ◽  
Short Term Forecasting ◽  
The Impact

The economic purpose of correlation-regression analysis is to determine the possible options for product competitiveness management, as well as an assessment of possible ways to achieve the desired result. The developed model can be used to improve planning and increase the level of product competitiveness. The forecast of results, though for the short term, gives the chance to learn about the prospects of obtaining the appropriate level of competitiveness of products in accordance with the degree of application of the impact on it. The forecast is dynamic and adapts to changes based on the latest data. The proposed model can be integrated into the existing decision support system to increase the competitiveness of products. In addition, correlation-regression analysis makes it possible to estimate the current situation using a regression equation. The mathematical reflection of the study of product competitiveness is the economic-mathematical model, which determines its functioning and assessment of changes in its effectiveness in the event of possible changes in the characteristics of economic activity. The parameters of economic models are estimated using the methods of mathematical statistics according to real statistical information. The task of correlation-regression analysis is to construct and analysis of the economic-mathematical model of the regression equation (correlation equation, which reflects the dependence of the resultant feature on several factor features and gives an estimate of the degree of connection density. Using data on the magnitude and direction of action of the analyzed factors, you can get the data that can be obtained to assess the relevant impact on the current level of product competitiveness. That is, such an analysis is a powerful and flexible tool for studying the relationships between product competitiveness indicators. The use of this method makes it possible to better understanding of the level of influence of factors on the competitiveness of products, and, accordingly, learn to manage the processes that take place, as well as more accurately predict their further interaction. These studies are important for the formation and implementation of management decisions to increase the competitiveness of products, because it narrows the choice of indicators with the greatest impact on its level. The ability to determine short-term forecasting of such impacts makes it possible to determine regional perspectives under the conditions of implemented measures.

scholarly journals Ionosphere dynamics over Europe and western Asia during magnetospheric substorms 1998–99

2003 ◽  
Vol 21 (5) ◽  
pp. 1141-1151 ◽  
Author(s):  
D. V. Blagoveshchensky ◽  
O. A. Maltseva ◽  
A. S. Rodger
Keyword(s):  
Short Term ◽  
Magnetospheric Substorms ◽  
Expansion Phase ◽  
Radio Science ◽  
Ionospheric Physics ◽  
Western Asia ◽  
Storms And Substorms ◽  
Temporal And Spatial ◽  
Using Data ◽  
Short Term Forecasting

Abstract. The temporal and spatial behaviour of the ionospheric parameters foF2 and h'F during isolated substorms are examined using data from ionospheric stations distributed across Europe and western Asia. The main purpose is finding the forerunners of the substorm disturbances and a possible prediction of these disturbances. During the period from March 1998 to March 1999, 41 isolated substorms with intensities I = 60 - 400 nT were identified and studied. The study separated occasions when the local magnetometers were affected by the eastward electrojet (positive substorms) from those influenced by the westward electrojet (negative substorms). The deviations of the ionospheric parameters from their monthly medians (DfoF2 and Dh'F) have been used to determine the variations through the substorm. Substorm effects occurred simultaneously (< 1 h) across the entire observatory network. For negative substorms, DfoF2-values increase > 6 h before substorm onset, To, reaching a maximum 2–3 h before To. A second maximum occurs 1–2 h after the end of the substorm. The Dh'F values 3–4 h before To have a small minimum but then increase to a maximum at To. There is a second maximum at the end of the expansion phase before dh'F drops to a minimum 2–3 h after ending the expansion phase. For positive substorms, the timing of the first maximum of the dfoF2 and dh'F values depends on the substorm length – if it is longer, the position is closer to To. The effects on the ionosphere are significant: DfoF2 and Dh'F reach 2–3 MHz (dfoF2 = 50–70% from median value) and 50–70 km (D h'F = 20–30% from median value), respectively. Regular patterns of occurrence ahead of the first substorm signature on the magnetometer offer an excellent possibility to improve short-term forecasting of radio wave propagation conditions.Key words. Ionosphere (ionospheric disturbances) – Magnetospheric physics (storms and substorms) Radio science (ionospheric physics)

scholarly journals Research on Long Short-Term Decision-Making System for Excavator Market Demand Forecasting Based on Improved Support Vector Machine

2021 ◽  
Vol 11 (14) ◽  
pp. 6367
Author(s):  
Bin Zhang ◽  
Teng Yang ◽  
Haocen Hong ◽  
Guozan Cheng ◽  
Huayong Yang ◽  
...  
Keyword(s):  
Decision Making ◽  
Demand Forecasting ◽  
Classification Error ◽  
Support Vector ◽  
Forecasting Model ◽  
Model Parameters ◽  
Short Term ◽  
Decision Making System ◽  
Short Term Forecasting

Future demand forecasting of the excavators is of great significance to guide the supply and marketing plan. For a long time, market forecasting of the construction machinery is regarded as short-term forecasting, which lacks the analysis of macro-marketing law and cannot reflect the true law of market development. In this paper, a decision-making system based on both long-term and short-term features was proposed. The interval classification and recursive feature elimination were used to select the main factors that affect the demand of excavators. Then a support vector regression model based on decomposition synthesis (DS-SVR) was developed to forecast the long-term features, and a model combined with a seasonal autoregressive integrated moving average model (SARIMA) was developed to forecast the short-term features. Finally, the differential evolution algorithm (DE) was applied to optimize model parameters. The performance of the forecasting model was tested using the marketing data of a typical enterprise. The results showed that the total error rate of the forecasting model for the one-year long-term forecasting is 26.61%, and the classification error of forecasting of the three-month short-term forecasting are 13.65%, 18.83%, and 19.62%, respectively, which are superior to the SVR forecasting model and the SARIMA forecasting model.

scholarly journals Analytics for Investigation of Disease Outbreaks: Web-Based Analytics Facilitating Situational Awareness in Unfolding Disease Outbreaks (Preprint)

2018 ◽  
Author(s):  
Nileena Velappan ◽  
Ashlynn Rae Daughton ◽  
Geoffrey Fairchild ◽  
William Earl Rosenberger ◽  
Nicholas Generous ◽  
...  
Keyword(s):  
Situational Awareness ◽  
Disease Outbreaks ◽  
Short Term ◽  
Web Based ◽  
Early Stages ◽  
Case Count ◽  
Similarity Algorithm ◽  
Using Data ◽  
Short Term Forecasting ◽  
Disease Specific

BACKGROUND Information from historical infectious disease outbreaks provides real-world data about outbreaks and their impacts on affected populations. These data can be used to develop a picture of an unfolding outbreak in its early stages, when incoming information is sparse and isolated, to identify effective control measures and guide their implementation. OBJECTIVE This study aimed to develop a publicly accessible Web-based visual analytic called Analytics for the Investigation of Disease Outbreaks (AIDO) that uses historical disease outbreak information for decision support and situational awareness of an unfolding outbreak. METHODS We developed an algorithm to allow the matching of unfolding outbreak data to a representative library of historical outbreaks. This process provides epidemiological clues that facilitate a user’s understanding of an unfolding outbreak and facilitates informed decisions about mitigation actions. Disease-specific properties to build a complete picture of the unfolding event were identified through a data-driven approach. A method of analogs approach was used to develop a short-term forecasting feature in the analytic. The 4 major steps involved in developing this tool were (1) collection of historic outbreak data and preparation of the representative library, (2) development of AIDO algorithms, (3) development of user interface and associated visuals, and (4) verification and validation. RESULTS The tool currently includes representative historical outbreaks for 39 infectious diseases with over 600 diverse outbreaks. We identified 27 different properties categorized into 3 broad domains (population, location, and disease) that were used to evaluate outbreaks across all diseases for their effect on case count and duration of an outbreak. Statistical analyses revealed disease-specific properties from this set that were included in the disease-specific similarity algorithm. Although there were some similarities across diseases, we found that statistically important properties tend to vary, even between similar diseases. This may be because of our emphasis on including diverse representative outbreak presentations in our libraries. AIDO algorithm evaluations (similarity algorithm and short-term forecasting) were conducted using 4 case studies and we have shown details for the Q fever outbreak in Bilbao, Spain (2014), using data from the early stages of the outbreak. Using data from only the initial 2 weeks, AIDO identified historical outbreaks that were very similar in terms of their epidemiological picture (case count, duration, source of exposure, and urban setting). The short-term forecasting algorithm accurately predicted case count and duration for the unfolding outbreak. CONCLUSIONS AIDO is a decision support tool that facilitates increased situational awareness during an unfolding outbreak and enables informed decisions on mitigation strategies. AIDO analytics are available to epidemiologists across the globe with access to internet, at no cost. In this study, we presented a new approach to applying historical outbreak data to provide actionable information during the early stages of an unfolding infectious disease outbreak.

The causes of poverty in Russia

2020 ◽  
Vol 13 (1) ◽  
pp. 21-36
Author(s):  
I.S. Ivanchenko
Keyword(s):  
Regression Analysis ◽  
Regional Development ◽  
Financial Market ◽  
Russian Federation ◽  
Short Term ◽  
Financial Situation ◽  
Macroeconomic Variables ◽  
Development Plans ◽  
The Russian Federation ◽  
Short Term Forecasting

Subject. This article analyzes the changes in poverty of the population of the Russian Federation. Objectives. The article aims to identify macroeconomic variables that will have the most effective impact on reducing poverty in Russia. Methods. For the study, I used the methods of logical, comparative, and statistical analyses. Results. The article presents a list of macroeconomic variables that, according to Western scholars, can influence the incomes of the poorest stratum of society and the number of unemployed in the country. The regression analysis based on the selected variables reveals those ones that have a statistically significant impact on the financial situation of the Russian poor. Relevance. The results obtained can be used by the financial market mega-regulator to make anti-poverty decisions. In addition, the models built can be useful to the executive authorities at various levels for short-term forecasting of the number of unemployed and their income in drawing up regional development plans for the areas.

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