This paper presents a neuro-based approach for annual transport energy demand forecasting by several socio-economic indicators. In order to analyze the influence of economic and social indicators on the transport energy demand, gross domestic product (GDP), population and total number of vehicles are selected. This approach is structured as a hierarchical artificial neural networks (ANNs) model based on the supervised multi-layer perceptron (MLP), trained with the back-propagation (BP) algorithm. This hierarchical ANNs model is designed properly. The input variables are transport energy demand in the last year, GDP, population and total number of vehicles. The output variable is the energy demand of the transportation sector in Million Barrels Oil Equivalent (MBOE). This paper proposes a hierarchical artificial neural network by which the inputs to the ending level are obtained as outputs of the starting levels. Actual data of Iran from 1968-2007 is used to train the hierarchical ANNs and to illustrate capability of the approach in this regard. Comparison of the model predictions with conventional regression model predictions shows its superiority. Furthermore, the transport energy demand of Iran for the period of 2008 to 2020 is estimated.
This study presents different types of neural network algorithm based model forecasting gas consumption for residential and commercial consumers in Istanbul in Turkey. Using seven neural networks algorithms as forecasting models, we tried to find the best solution on forecasting of monthly natural gas consumption. These models were validated and tested on real monthly data from a distribution area covering two different regions of Anatolian and European sides in Istanbul. The analysis of results obtained for training and test sets show that the seven proposed artificial neural network models could be useful for the natural gas consumption forecast problem. It was shown that a conjugate gradient descent neural network model presented a more efficient solution than the other models.
From 29th March to 09th April 2006, the Morava catchment in the Danube River basin was hit by severe flooding caused by snow melting and rainfall. The floods affected settlements and agricultural lands in Slovakia, Czech Republic and Austria. In the downstream Morava, 100-year flood and more peak discharges were observed. The European Flood Alert System (EFAS), under development and running in pre-operational mode at the Joint Research Center of the European Commission, in partnership with Member States’ authorities and meteorological data providers, forecasted the event more than five days in advance. This paper investigates the performance of EFAS hydrologic forecasts for this event. Forecasts based on deterministic and probabilistic weather forecasts are presented and verified against observed data. The analysis showed that forecasts based on probabilistic weather ensembles were able to detect an earlier signal of the flood event. The lack of consistence between simulations based on different medium-range weather forecasts was the main reason for a late diffusion of EFAS forecasts to its partner in Slovakia, with an impact on the value of the forecasts as a pre-alert. The potential benefit of ensemble hydrologic forecasts to early flood warning and increased preparedness is highlighted. and V období 29. marca až 9. apríla 2006 zasiahla povodie Moravy, ktoré je súčasťou povodia Dunaja ničivá povodeň, zapríčinená prevažne topením sa snehu v kombinácii s pomerne výdatnou zrážkovou činnosťou. Povodeň spôsobila škody na obydliach a poľnohospodárskej pôde v Českej republike, Rakúsku a aj na Slovensku. V dolnej časti toku Morava bol zaznamenaný kulminačný prietok s dobou opakovania viac ako 100 rokov. Európsky povodňový varovný systém, vyvinutý a prevádzkovaný v tzv. predoperačnom režime JRC EU v spolupráci s členskými štátmi, ktoré sú aj poskytovateľmi hydrometeorologických údajov, prognózoval túto rozsiahlu povodňovú epizódu s predstihom 5 dní. Príspevok sa zameriava na skúmanie realizácie hydrologických predpovedí z EFAS systému pre uvedenú povodňovú udalosť. Predpovede vypočítané na základe deterministických a pravdepodobnostných predpovedí počasia sú verifikované pozorovanými údajmi. Analýza výsledkov ukázala, že predpovede robené na základe pravdepodobnostných ansámblových predpovedí vývoja počasia boli schopné dať signál, že sa povodňová udalosť stane, s väčším intervalom predstihu. V dôsledku nekonzistencie vyhodnocovaných predpovedí bola slovenskému partnerovi EFAS-u odoslaná oneskorene. Príspevok poukázal tiež na skutočnosť, že využitie ansámblov pre hydrologické predpovede a včasné varovanie pred nebezpečenstvom povodní umožňuje v dostatočnom predstihu zlepšiť pripravenosť na tieto udalosti.
An empirical model for forecasting electrie power consumption is forrnulated. The research concerns the preparation and optimal selection of characteristic variables. Prototype patteriis of eleetric power consumption over a day are described by proper by encoding the day-types and their self-organised adaptation to the data recorded in the past. In this procedure, holidays are treated by specific prototype patterns. The influence of the environmental temperature on the consumed power is accounted for by including the extrerne vahies of temperature in a day into prototype patterns. These patterns are employed as parameters of a norrnalised radial basis function neural network, which is used to forecasting the consumption process. The performance of forecasting and the applicability of various input variables is tested, based on one- and four-year-long records of electrie power consumption in Slovenia.
Air transportation between Europe and the U.S. is becoming more and more significant. It can only hardly be replaced by other means of transportation, since its biggest advantages include speed and reliability. Air transportation forecasting is important for planning the development of airports and related infrastructure, and of course also for air carriers. Therefore, it is important to forecast the number of flights between selected airports in Europe and the U.S. and the number of transported persons. A gravity model is usually used for this forecasting. Determination of coefficients which significantly affect results of the formulas used in the gravity model is crucial. Coefficients are, as a rule, computed by an iterative algorithm implementing the gradient method. This technique has some limitations if the state space is inappropriate. Moreover, the exponent parameter in the formula is obviously fixed. We have chosen the new method of differential evolution to determine the gravity model coefficient. Differential evolution works with populations similarly to other evolution algorithms. It is suitable for solving complex numerical problems. The suggested methodology can be helpful for various airlines to forecast demand and plan new long-haul routes.
Forecasting the river flow level and volumes are essential to making the most efficient use of rivers and in minimizing damages to flood. A relationship between the released discharges from Mosul dam and river levels in Mosul station is predicted with high correlation coefficient for the two periods within the year (rainy and non-rainy months). A time series technique analysis for predicting the best relevant statistical model for future forecasting of Tigris River levels within the river reach between Mosul dam and Mosul city was applied. Deterministic prediction of the water levels of Tigris River within the reach between Mosul dam and Mosul city will help to avoid the increasing of the water level within the river reach to minimize the damages which may occur due to inundation areas. The average monthly Tigris River stages of Mosul station for the two periods before and after Mosul dam construction are constant with a reduction in the average value of the maximum water stages and increasing the average value of the minimum water stages after Mosul dam construction. Through the statistical analysis of the time series of the available river stages data at Mosul station, a repetition in the annual cycle in the water stages before Mosul dam construction and a decreasing trend through this period was observed. Winter model is the most suitable time series model to forecast the Tigris River stages or any missing data in the future in the Tigris River reach between Mosul dam and Mosul city. and Predpoveď vodných stavov a prietokov vodných tokov je podstatná pre ich efektívne využitie a minimalizáciu povodňových škôd. Závislosť medzi výtokom z priehrady Mosul a stavom vody v rieke v stanici v Mosule bola určená s vysokou hodnotou súčiniteľa korelácie pre dve obdobia roka (zrážkové a bezzrážkové obdobie). Na analýzu časových radov sme použili najvhodnejší štatistický model, ktorý umožnil predpoveď vodných stavov Tigrisu medzi priehradou Mosul a mestom Mosul. Deterministická predpoveď vodných stavov Tigrisu môže pomôcť minimalizovať škody v záplavovom území. Priemerné mesačné stavy vody v hydrologickej stanici v Mosule na rieke Tigris pre dve obdobia - pred vybudovaním priehrady Mosul a po ňom - sa nemenia, ale priemerné hodnoty maximálnych stavov vody po vybudovaní priehrady v Mosule sa znížili, naopak minimálne hodnoty vodných stavov sa zvyšujú. Štatistickou analýzou časových radov dostupných vodných stavov v stanici Mosul sa zistila nemennosť vodných stavov počas ich ročného cyklu pred vybudovaním priehrady Mosul a po jej vybudovaní. Najvhodnejším modelom časových radov pre predpoveď vodných stavov v rieke Tigris, alebo pre predpoveď chýbajúcich hodnôt vodných stavov v úseku rieky medzi priehradou a mestom Mosul, sa ukázal zimný model.
Short term streamflow forecasting is important for operational control and risk management in hydrology. Despite a wide range of models available, the impact of long range dependence is often neglected when considering short term forecasting. In this paper, the forecasting performance of a new model combining a long range dependent autoregressive fractionally integrated moving average (ARFIMA) model with a wavelet transform used as a method of deseasonalization is examined. It is analysed, whether applying wavelets in order to model the seasonal component in a hydrological time series, is an alternative to moving average deseasonalization in combination with an ARFIMA model. The one-to-ten-steps-ahead forecasting performance of this model is compared with two other models, an ARFIMA model with moving average deseasonalization, and a multiresolution wavelet based model. All models are applied to a time series of mean daily discharge exhibiting long range dependence. For one and two day forecasting horizons, the combined wavelet - ARFIMA approach shows a similar performance as the other models tested. However, for longer forecasting horizons, the wavelet deseasonalization - ARFIMA combination outperforms the other two models. The results show that the wavelets provide an attractive alternative to the moving average deseasonalization.