1. Methods and models for electric load forecasting : a comprehensive reviewMahmoud A. Hammad, Borut Jereb, Bojan Rosi, Dejan Dragan, 2020, izvirni znanstveni članek Opis: Electric load forecasting (ELF) is a vital process in the planning of the electricity industry and plays a crucial role in electric capacity scheduling and power systems management and, therefore, it has attracted increasing academic interest. Hence, the accuracy of electric load forecasting has great importance for energy generating capacity scheduling and power system management. This paper presents a review of forecasting methods and models for electricity load. About 45 academic papers have been used for the comparison based on specified criteria such as time frame, inputs, outputs, the scale of the project, and value. The review reveals that despite the relative simplicity of all reviewed models, the regression analysis is still widely used and efficient for long-term forecasting. As for short-term predictions, machine learning or artificial intelligence-based models such as Artificial Neural Networks (ANN), Support Vector Machines (SVM), and Fuzzy logic are favored.
Ključne besede: methods, models, electric load forecasting, modeling electricity loads, electricity industry, power management, logistics
Objavljeno v DKUM: 22.08.2024; Ogledov: 95; Prenosov: 8
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2. Forecasting US tourists' inflow to Slovenia by modified holt-winters damped model : a case in the tourism industry logistics and supply chainsDejan Dragan, Abolfazl Keshavarzsaleh, Tomaž Kramberger, Borut Jereb, Maja Rosi, 2019, izvirni znanstveni članek Opis: Forecasting is important in many branches of logistics, including the logistics related to Tourism supply chains. With an increasing inflow of American tourists, planning and forecasting the US tourists' inflow to Slovenia have gained far more importance attention amongst scholars and practitioners. This study, therefore, was conducted to forecast the American tourists' inflow to Slovenia using one of the predictive models based on the exponential smoothing approach, namely Holt-Winters damped additive (HWDA) exponential smoothing method. The model was modified by several improvements, while the obtained results were generalized to other supply chain components. The results show that the forecasting system can predict well the observed inflow, while the methodology used to derive the model might have enriched the plethora of existing practical forecasting approaches in the tourism domain. Benchmarking demonstrates that the proposed model outperforms a competitive ARIMA model and official forecasts. The practical implications are also discussed in this paper.
Ključne besede: tourism, forecasting, Holt-Winters model, US tourists, supply chains, logistics
Objavljeno v DKUM: 22.08.2024; Ogledov: 42; Prenosov: 8
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3. Road freight transport forecasting : a fuzzy Monte-Carlo simulation-based model selection approachDejan Dragan, Simona Šinko, Abolfazl Keshavarzsaleh, Maja Rosi, 2022, izvirni znanstveni članek Opis: As important as the classical approaches such as Akaike's AIC information and Bayesian BIC criterion in model-selection mechanism are, they have limitations. As an alternative, a novel modeling design encompasses a two-stage approach that integrates Fuzzy logic and Monte Carlo simulations (MCSs). In the first stage, an entire family of ARIMA model candidates with the corresponding information-based, residual-based, and statistical criteria is identified. In the second stage, the Mamdani fuzzy model (MFM) is used to uncover interrelationships hidden among previously obtained models criteria. To access the best forecasting model, the MCSs are also used for different settings of weights loaded on the fuzzy rules. The obtained model is developed to predict the road freight transport in Slovenia in the context of choosing the most appropriate electronic toll system. Results show that the mechanism works well when searching for the best model that provides a well-fit to the real data.
Ključne besede: forecasting road transport, electronic toll system, Monte Carlo simulation, ARIMA models, logistics
Objavljeno v DKUM: 11.06.2024; Ogledov: 135; Prenosov: 6
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4. Radon anomalies in soil gas caused by seismic activityBoris Zmazek, Mladen Živčić, Ljupčo Todorovski, Sašo Džeroski, Janja Vaupotič, Ivan Kobal, 2004, izvirni znanstveni članek Opis: At the Orlica fault in the Krško basin, combined barasol detectors were buried in six boreholes, two along the fault itself and four on either side of it, to measure and record radon activity, temperature and pressure in soil gas every 60 minutes for four years. Data collected have been analysed in a manner aimed at distinguishing radon anomalies resulting from environmental parameters (air and soil temperature, barometric pressure, rainfall) from those caused solely by seismic events. The following approaches have been used to identify anomalies: (i) ± 2σ deviation of radon concentration from the seasonal average, (ii) correlation between time gradients of radon concentration and barometric pressure, and (iii) prediction with regression trees within a machine learning program. In this paper results obtained with regression trees are presented. A model has been built in which the program was taught to predict radon concentration from the data collected during the seismically inactive periods when radon is presumably influenced only by environmental parameters. A correlation coefficient of 0.83 between measured and predicted values was obtained. Then, the whole data time series was included and a significantly lowered correlation was observed during the seismically active periods. This reduced correlation is thus an indicator of seismic effect.
Ključne besede: radon in soil gas, environmental parameters, earthquakes, correlation, regression trees, forecasting
Objavljeno v DKUM: 15.05.2018; Ogledov: 1784; Prenosov: 88
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5. Container throughput forecasting using dynamic factor analysis and ARIMAX modelMarko Intihar, Tomaž Kramberger, Dejan Dragan, 2017, izvirni znanstveni članek Opis: 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.
Ključne besede: container throughput forecasting, ARIMAX model, dynamic factor analysis, exogenous macroeconomic indicators, time series analysis
Objavljeno v DKUM: 12.12.2017; Ogledov: 2198; Prenosov: 426
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6. Forecasting the primary demand for a beer brand using time series analysisDanjel Bratina, Armand Faganel, 2008, izvirni znanstveni članek Opis: Market research often uses data (i.e. marketing mix variables) that is equally spaced over time. Time series theory is perfectly suited to study this phenomena's dependency on time. It is used for forecasting and causality analysis, but their greatest strength is in studying the impact of a discrete event in time, which makes it a powerful tool for marketers. This article introduces the basic concepts behind time series theory and illustrates its current application in marketing research. We use time series analysis to forecast the demand for beer on the Slovenian market using scanner data from two major retail stores. Before our analysis, only broader time spans have been used to perform time series analysis (weekly, monthly, quarterly or yearly data). In our study we analyse daily data, which is supposed to carry a lot of ‘noise’. We show that - even with noise carrying data - a better model can be computed using time series forecasting, explaining much more variance compared to regular regression. Our analysis also confirms the effect of short term sales promotions on beer demand, which is in conformity with other studies in this field.
Ključne besede: market research, time series forecasting, beer demand
Objavljeno v DKUM: 30.11.2017; Ogledov: 1235; Prenosov: 360
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7. Obvladovanje tveganj pri »peer to peer« posojilihAndrej Blagotinšek, 2017, magistrsko delo Opis: Nove digitalne tehnologije botrujejo procesu preoblikovanja obstoječih vrednostih verig finančnih produktov oz. storitev. »P2P« posojila so nov in inovativen način tako investiranja presežkov finančnih sredstev kot tudi prejemanja finančnega kapitala. Število tovrstnih posojil konstantno raste, vendar posojilodajalci niso profesionalni investitorji. Posojilodajalci prevzemajo veliko tveganje, saj so »P2P« posojila izdana brez zavarovanja. V ta namen »P2P« platforme izdajajo historične podatke o posojilojemalcih.
V delu se osredotočamo na identifikacijo tveganj, ki so prisotna pri tovrstnem investiranju in na napovedovanje možnosti neplačil posojil. Empirična študija analizira podatke pridobljene iz platforme Bondora (N=1823) od leta 2009 do 2015. Opravili smo statistično analizo spremenljivk. Razvili smo Logit model za napovedovanje neplačil. Kakovost modela smo preverjali z ROC krivuljo, optimizacijo modela pa na osnovi uravnoteženja klasifikacijske natančnosti, kjer smo dololčili optimalno presečno vrednost. Rezultati so pokazali, da kreditni model za napovedovanje neplačil zmanjšuje verjetnost finančne izgube pri »P2P« investiranju.
Ključne besede: kreditno tveganje, verjetnost neplačila, »P2P« posojila, LOGIT model, obvladovanje tveganj, C25 Discrete Regression and Qualitative Choice Models, G21 Banks, G17, Financial Forecasting and Simulation
Objavljeno v DKUM: 27.10.2017; Ogledov: 2426; Prenosov: 332
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8. Intra-minute cloud passing forecasting based on a low cost iot sensor - a solution for smoothing the output power of PV power plantsPrimož Sukič, Gorazd Štumberger, 2017, izvirni znanstveni članek Opis: Clouds moving at a high speed in front of the Sun can cause step changes in the output power of photovoltaic (PV) power plants, which can lead to voltage fluctuations and stability problems in the connected electricity networks. These effects can be reduced effectively by proper short-term cloud passing forecasting and suitable PV power plant output power control. This paper proposes a low-cost Internet of Things (IoT)-based solution for intra-minute cloud passing forecasting. The hardware consists of a Raspberry PI Model B 3 with a WiFi connection and an OmniVision OV5647 sensor with a mounted wide-angle lens, a circular polarizing (CPL) filter and a natural density (ND) filter. The completely new algorithm for cloud passing forecasting uses the green and blue colors in the photo to determine the position of the Sun, to recognize the clouds, and to predict their movement. The image processing is performed in several stages, considering selectively only a small part of the photo relevant to the movement of the clouds in the vicinity of the Sun in the next minute. The proposed algorithm is compact, fast and suitable for implementation on low cost processors with low computation power. The speed of the cloud parts closest to the Sun is used to predict when the clouds will cover the Sun. WiFi communication is used to transmit this data to the PV power plant control system in order to decrease the output power slowly and smoothly.
Ključne besede: photovoltaic power plant, cloud passing forecasting, algorithm, sensor, Raspberry Pi, camera, wide-angle lens, optical filters, internet of things
Objavljeno v DKUM: 20.07.2017; Ogledov: 2772; Prenosov: 485
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9. Statistics for business and economicsDavid Ray Anderson, Dennis J. Sweeney, Thomas Arthur Williams, Jim Freeman, Eddie ShoesmithKljučne besede: statistics, statistical methods, commercial business, economy, probability, distributions, sampling, analysis of variance, experimental design, regression analysis, forecasting, analysis
Objavljeno v DKUM: 06.06.2012; Ogledov: 1654; Prenosov: 39
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10. Marketing theory : a student text2000, učbenik za višje in visoke šole Ključne besede: marketing, theory, philosophy, marketing strategy, economics, consumer, consumer psychology, behavior, culture, market, segmantation, positioning, communication, services, business ethics, non-profit organizations, marketing mix, historical overwiev, postmodernity, future, forecasting, textbooks, miscellanies, collective works
Objavljeno v DKUM: 01.06.2012; Ogledov: 2593; Prenosov: 167
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