GASOLINE CONSUMPTION PREDICTION VIA DATA MINING TECHNIQUE

Due to the increasing dependence of human life on energy, it plays a crucial role in the functioning of the various economic sectors of the countries, potentially and actually. Fuel products, especially gasoline, given their importance in the transportation sector, play major roles in the economic growth and development of countries. Hence, the authorities in each country have to control the fuel supply and demand parameters accurately with a more accurate prediction of fuel consumption and proper planning in the direction of consumption. The purpose of this study is to find appropriate methods and approaches for forecasting gasoline consumption in Tehran using data mining methods. For this purpose, daily consumption data of gasoline stations were collected in 5 different regions of Tehran during the period of 2008-2013. Then, these numbers were predicted on a daily, weekly, monthly, and seasonal basis for analyzing the consumption at different time intervals. The standardization method was also used to match the scales. After data pre-processing, gasoline consumption was predicted using the multi-layer perceptron (MLP) neural network method. The gasoline consumption forecast was evaluated based on the mean squared error (MSE), mean, and mean absolute error (MAE) criteria. The results indicate that the artificial neural network (ANN) can accurately predict gasoline consumption in five different regions of Tehran.

Gasoline in the Voter’s Pocketbook: Driving Times to Work and the Electoral Implications of Gasoline Price Fluctuations

Gasoline prices are often a heated topic during presidential election campaigns in the United States. Yet, presidents have limited control over gasoline prices. Do voters reward or punish the president for changes in gasoline prices? Why might voters blame the president for an outcome beyond direct presidential control? This study addresses these questions by testing the effects of gasoline prices on pocketbook retrospection by voters. To capture the personal economic burden of gasoline prices, we rely on average driving times to work, given the inelastic nature of gasoline consumption for commuting. The results provide evidence for pocketbook voting: constituencies with longer average driving times to work are more likely to hold the president accountable for gasoline price increases. These findings have broader implications regarding electoral accountability and rationality in voting.

A Data-Trait-Driven Rolling Decomposition-Ensemble Model for Gasoline Consumption Forecasting

In order to predict the gasoline consumption in China, this paper propose a novel data-trait-driven rolling decomposition-ensemble model. This model consists of five steps: the data trait test, data decomposition, component trait analysis, component prediction and ensemble output. In the data trait test and component trait analysis, the original time series and each decomposed component are thoroughly analyzed to explore hidden data traits. According to these results, decomposition models and prediction models are selected to complete the original time series data decomposition and decomposed component prediction. In the ensemble output, the ensemble method corresponding to the decomposition method is used for final aggregation. In particular, this methodology introduces the rolling mechanism to solve the misuse of future information problem. In order to verify the effectiveness of the model, the quarterly gasoline consumption data from four provinces in China are used. The experimental results show that the proposed model is significantly better than the single prediction models and decomposition-ensemble models without the rolling mechanism. It can be seen that the decomposition-ensemble model with data-trait-driven modeling ideas and rolling decomposition and prediction mechanism possesses the superiority and robustness in terms of the evaluation criteria of horizontal and directional prediction.

A Fractional Discrete Grey Model with Particle Swarm Optimizer and Its Applications in Forecasting the Gasoline Consumption in Chongqing China

Forecasting gasoline consumption is of great significance for formulating oil production, foreign trade policies, and ensuring the balance of domestic refined oil supply. Based on grey system theory, a fractional accumulation operator is constructed to optimize the accumulation method of the traditional discrete grey model, and the Particle Swarm Optimization algorithm is used to solve the fractional nonlinear parameters. This model was used in the prediction of gasoline consumption in Chongqing, China, and compared with the existing 7 models. The results show that the fractional discrete grey model optimized by PSO has better prediction accuracy. The fractional discrete grey model optimized by PSO can be used as a quantitative method in the field of energy forecasting.

A Two-Level Model for Traffic Signal Timing and Trajectories Planning of Multiple CAVs in a Random Environment

Connected and automated vehicles (CAVs) trajectories not only provide more real-time information by vehicles to infrastructure but also can be controlled and optimized, to further save travel time and gasoline consumption. This paper proposes a two-level model for traffic signal timing and trajectories planning of multiple connected automated vehicles considering the random arrival of vehicles. The proposed method contains two levels, i.e., CAVs’ arrival time and traffic signals optimization, and multiple CAVs trajectories planning. The former optimizes CAVs’ arrival time and traffic signals in a random environment, to minimize the average vehicle’s delay. The latter designs multiple CAVs trajectories considering average gasoline consumption. The dynamic programming (DP) and the General Pseudospectral Optimal Control Software (GPOPS) are applied to solve the two-level optimization problem. Numerical simulation is conducted to compare the proposed method with a fixed-time traffic signal. Results show that the proposed method reduces both average vehicle’s delay and gasoline consumption under different traffic demand significantly. The average reduction of vehicle’s delay and gasoline consumption are 26.91% and 10.38%, respectively, for a two-phase signalized intersection. In addition, sensitivity analysis indicates that the minimum green time and free-flow speed have a noticeable effect on the average vehicle’s delay and gasoline consumption.

Forecast of Energy Consumption Based on FGM(1, 1) Model

The normal supply of energy is related to the stable development of the economy and society. Forecasting energy consumption helps prepare for the normal supply of energy. In the study of energy consumption forecasting, different scholars have used different forecasting models. This paper uses five-year energy consumption data in the Beijing-Tianjin-Hebei region and uses the grey fractional FGM(1, 1) model to analyze the next six years. Then, the energy consumption of three places is predicted. The advantage of the grey score FGM(1, 1) model is that it can get more accurate prediction results based on a small amount of information. In this study, relatively outdated information affects the accuracy of prediction results. However, other prediction models have great limitations on data. Choosing the grey number fractional model for prediction research can get a more reasonable prediction result. We use the FGM(1, 1) model to make predictions and get the prediction results. In Beijing, the growth rate of natural gas consumption has slowed down and will be basically stable by 2023. The average annual deceleration of coal consumption is 32%. The average annual deceleration of coke consumption is 10%. Crude oil consumption decreased by 6.3% annually. Gasoline consumption is slowly increasing. The consumption of kerosene increased about 8% annually. Diesel consumption is slowly decreasing. Fuel oil consumption is reduced by 17% annually. The average annual growth rate of power consumption exceeds 6%. In Tianjin, the annual growth rate of natural gas consumption is about 5%. Coal consumption is reduced by about 8% every year. The average annual deceleration of coke consumption is 7%. Crude oil consumption decreased by 2.4% annually. Gasoline consumption is slowly decreasing. The consumption of kerosene has increased by about 20% annually. Diesel consumption is slowly decreasing. Fuel oil consumption is reduced by 20% annually. Electricity consumption is slowly increasing. In Hebei Province, the annual growth rate of natural gas consumption is about 15%. Annual coal consumption is reduced by about 3%. Coke consumption remained stable. Crude oil consumption is reduced by 3% annually. Gasoline consumption is slowly increasing, and kerosene consumption has increased by about 31% annually. Diesel consumption is reduced by about 3% annually. Fuel oil consumption remained stable. Electricity consumption is slowly increasing.

Improvement of vehicles operation by application of gasoline modified with nano-additive

The results of studies of the influence of the developed nano-additive on gasoline consumption, acoustic vibrations and vibration in the engine are presented. The conducted tests of the effect of the nano-additive application on fuel efficiency in highway driving conditions showed a decrease in the specific consumption of gasoline modified with a nano-additive up to 14.08 % relative to standard gasoline. Accordingly, the reduction in gasoline consumption will reduce emissions of toxic substances and greenhouse gases. It was found that the introduction of nano-additive into gasoline, which improves the combustion process, reduces the level of noise and vibration during vehicle operation and makes it possible to reduce the pollution of the acoustic environment. The use of gasoline modified with a nano-additive can significantly improve the environmental situation and reduce the consumption of scarce hydrocarbon fuels.