Papers by Keyword: Forecasting Model

Abstract: Energy is one of the most important inputs to maintain social and economic improvement in the countries. It is necessary that energy demand should be performed at the right time economically and be of good quality and respectful if increasing environmental consciousness in order to preserve national development and a high standard of living. Turkey's energy use is expected to increase by 50% over the next decade. Turkey's installed capacity has exceeded 88 GW as of January 2019, representing a threefold increase in 15 years. For this reason, an accurate prediction of the consumed energy is critical. Predictions of energy demand in developing countries show more deviations than in developed countries. The essential scope of this study is to develop a new electricity prediction model for Turkey, which has not been used in the literature before. In the study, the global system for mobile communications (gsm) subscribers, fertility rate and cultivated land per capita have been used for the first time in the literature as variables. Factors resulting from health-ecological problems as well as cultural, social and economic changes and differences in Turkey were included in the model to obtain more realistic results. The model was developed between 1975 and 2016, and 73 different economic and social variables were evaluated using artificial neural networks (ANN). The model was established by reducing the number of variables according to the weight ratio. Then, two different cases have been created and tested. Turkey’s electricity consumption has been predicted accurately until 2023 using SPSS Clementine software.

Abstract: A laboratory analysis of concrete samples requires significant experimental time and cost. In addition, advancement in data mining provide valuable tool for researchers to extract information regarding relations among experiment and physical properties in a more elaborate way to improve prediction models performance and guide concrete mix design. A 90 samples data set is developed and used in this research. The experiment is designed to study the effect of natural silica addition at different levels on physical properties of concrete mainly compressive strength. Compressive strength is measured after 3 and 28 days for different levels of milling time. Support vector regression and neural network models are developed for predicting the compressive strength of concrete using five input variables including silica additive fraction. The SVR model metrics are compared with ANN model and showed good correlation coefficient of 0.929 but less than ANN. The advantage of SVR over ANN is shown in the developed regression model which can be interpreted physically. The silica fraction variable ranked third after curing time and cement ratio variable which indicates its importance.

Abstract: Since Song and Chissom proposed fuzzy time series forecasting theory, already exceed in the 20 years. Scholars have proposed many fuzzy time series forecasting models, the prediction accuracy of historical simulation data continues to improve. Unfortunately has not hitherto given for fuzzy time series forecasting model about the data of unknown years. This paper presents an improved forecasting model of fuzzy time series. It may predict the historical simulation data, but also may predict the unknown year data.

Abstract: The grey theory is employed to establish the grey prediction-wind speed Weibull distribution model and calculate the Weibull distribution parameters according to the randomness and intermittence of the wind power output. The wind speed distribution of the wind farm and the effective wind power density are predicted accurately, the wind power and the electric fan efforts in generating capacity and other important data can be obtained according to the actual terrain wind farm wind speed data.

Abstract: Based on the analysis of some conventional fuzzy time series model,this paper proposes a new method by using a single constrained optimization to determine the interval length for improving the forecasts.The conventional fuzzy time series models are enhanced by forecast-weighted method in the forecasting.In the proposed model,which is evaluated by mean square error(MSE),fuzzy membership degrees are used to calculate forecast weights. The empirical results show that the proposed model outperforms than the conventional models.

Abstract: Hangzhou, the capital of Zhejiang province and a famous scenic tourist city in China, goes at the forefront of the country for its high real estate prices, which hold a very important position of orientation to pricing in the real estate markets of the Yangtze River Delta region and of the whole country as well. The price trend of Hangzhou's real estate is even related to the sustainable development of the city. This paper uses the macro data on the housing market in Hangzhou during 1999-2012 to establish a forecasting model which is based on BP neural network of genetic algorithm optimization. With MATLAB software exploited for programming and simulation, the prediction made by the model about the housing demand in Hangzhou and the subsequent re-examination show that the model has high precision. But due to the impact of the national macro-control policies on housing market, the predictive value of some years may fluctuate to a certain extent.

Abstract: There are several factors that can be used to predict a dengue fever outbreak. Almost all existing research approaches, however, usually exploit the use of a basic set of core attributes to forecast an outbreak, e.g. temperature, humidity, wind speed, and rainfall. In contrast, this research identifies new attributes to improve the prediction accuracy of the outbreak. The experimental results are analyzed using a correlation analysis and demonstrate that the density of dengue virus infection rate in female mosquitoes and seasons have strong correlation with a dengue fever outbreak. In addition, the research constructs a forecast model using Poisson regression analysis. The result shows the proposed model obtains significantly low forecasting error rate when compared it against the conventional model using only temperature, humidity, wind speed, and rainfall parameters.

Abstract: The working conditions of gas consumption of different types of users are analyzed in this paper and four gas load forecasting approaches are introduced. Meanwhile, the gas load of Changchun city is forecasted through different approaches.

Abstract: The reasonably and accurately prediction of tourism highway traffic demand of tourist attractions is a key issue in regional tourist planning and road network optimization. Based on the analysis of influence factors, this paper presented a method to calculate tourist highway traffic demand of tourist attractions through the number of visiting tourism of the tourist attraction. Then we compared the predicted value with the actual survey value, and a case study of Xingkai Lake had been analyzed and attempted to validate the establishment model. The presented tourist highway traffic demand model can provide a reference for regional tourist planning and road network optimization.

Abstract: In this study, the fluorescence in situ hybridization (FISH) was used to specifically detect the seeded bacteria Pseudomonas sp and Bacillus subtilis in the BEAC filter. For evaluating the cooperative interaction between the seeded bacteria, Lotka-Volterra model was introduced to calculate the data gained from FISH results. The parameter α measures how much species Pseudomonas sp. inhibits species Bacillus subtiliss growth, and β measures how much species Bacillus subtilis inhibits species Pseudomonas sp..α=0.0063<K₁/K₂ and β=0.73<K₂/K₁ in the top section of the BEAC filter.α=-0.13<K₁/K₂ and β=0.41<K₂/K₁ in the middle section of the BEAC filter.α=0.66<K₁/K₂ and β=0.30<K₂/K₁ in the bottom section of the BEAC filter. The seeded bacteria Pseudomonas sp. and Bacillus subtilis could coexist in the BEAC filter. The bio-enhanced technology used here is a promising approach to introduce and maintain the seeded bacteria.