• Energy Research

The technological development of buses with new alternative fuels is considered in this paper. Several types of fuels are considered as alternative-fuel modes, i.e., electricity, fuel cell (hydrogen), and methanol. Electric vehicles may be considered the alternative-fuel vehicles with the lowest air pollution. Hybrid electric vehicles provide an alternate mode, at least for the period of improving the technology of electric vehicles. A hybrid electric vehicle is defined as a vehicle with the conventional internal combustion engine and an electric motor as its major sources of power. Experts from different decision-making groups performed the multiple attribute evaluation of alternative vehicles. AHP is applied to determine the relative weights of evaluation criteria. TOPSIS and VIKOR are compared and applied to determine the best compromise alternative fuel mode. The result shows that the hybrid electric bus is the most suitable substitute bus for Taiwan urban areas in the short and median term. But, if the cruising distance of the electric bus extends to an acceptable range, the pure electric bus could be the best alternative.

Abstract This paper describes a computer-based decision support system (DSS) for evaluating quality of life (QOL) improvement in general, and technological and environmental impacts of energy planning and consumption in particular. Based primarily on information obtained from the Environmental Protection Administration and the Energy Committee, Ministry of Economic Affairs of ROC, the variations in economic growth and overall life quality changes in Taiwan may be quantitatively assessed and performance indexes for various socioeconomic, political, environmental, energy and technological aspects constructed using historical and extrapolated time series data. The interdependent relationships and cross-impacts among energy planning, patterns of energy production and consumption, and ‘trade-off’ decisions in environmental quality management may be objectively tested and analysed. This approach is designed to support and enhance strategical decision-makers in a more organized and efficient manner for semi-structured tasks and/or problem solving, assisted by a micro- or personal computer.

This study developed a novel model, HGA-SVR, for type of kernel function and kernel parameter value optimization in support vector regression (SVR), which is then applied to forecast the maximum electrical daily load. A novel hybrid genetic algorithm (HGA) was adapted to search for the optimal type of kernel function and kernel parameter values of SVR to increase the accuracy of SVR. The proposed model was tested at an electricity load forecasting competition announced on the EUNITE network. The results showed that the new HGA-SVR model outperforms the previous models. Specifically, the new HGA-SVR model can successfully identify the optimal type of kernel function and all the optimal values of the parameters of SVR with the lowest prediction error values in electricity load forecasting.

Abstract This paper emphasizes the evaluation of energy conservation strategies that are considered to be sensitive to modal choice by employing a disaggregate logit model. Five hundred households in Taipei city were selected and interviewed on weekdays between 26 December 1986 and 15 January 1987 by using a stratified random sampling process. By computing elasticities of the logit model, three kinds of energy conservation strategies – taxi-fares, exclusive bus lanes and gasoline price – were selected for sensitivity analysis. The results show that a passenger's modal choice and energy consumption in transport are not affected greatly by the three selected energy conservation strategies in the short term. Therefore it is recommended that other strategies are used, such as reducing trip generation, shortening trip length, improving fuel efficiency and increasing vehicle occupancy to reduce energy consumption in transport.

Credit scoring models have been widely studied in the areas of statistics, machine learning, and artificial intelligence (AI). Many novel approaches such as artificial neural networks (ANNs), rough sets, or decision trees have been proposed to increase the accuracy of credit scoring models. Since an improvement in accuracy of a fraction of a percent might translate into significant savings, a more sophisticated model should be proposed to significantly improving the accuracy of the credit scoring mode. In this paper, genetic programming (GP) is used to build credit scoring models. Two numerical examples will be employed here to compare the error rate to other credit scoring models including the ANN, decision trees, rough sets, and logistic regression. On the basis of the results, we can conclude that GP can provide better performance than other models.

Abstract The purposes of this study are to develop a representative driving cycle for motorcycles in metropolitan Taipei and to ascertain the emissions and fuel economy of the cycle. We collected extensive driving cycle data and proposed a methodology to develop a Taipei motorcycle driving cycle (TMDC). The characteristics of TMDC are high average acceleration and deceleration, high acceleration–deceleration changes and low average travel speed. Forty-five motorcycles were tested in a laboratory by using the ECE-40 and TMDC test procedure. The emissions of motorcycles tested by TMDC are higher than ECE, whether they are two-stroke or four-stroke engines. Furthermore, the CO and HC emissions of two-stroke engine motorcycles are higher than four-stroke engine motorcycles, and the NOx emission of two-stroke engine motorcycles are lower than four-stroke engine motorcycles, whether they are tested by TMDC or ECE. The fuel economy of two-stroke engine motorcycles tested by TMDC is lower than ECE, but the fuel economy of four-stroke engine motorcycles tested by TMDC is higher than ECE. A linear regression of TMDC in terms of ECE emissions shows them to be highly correlated, as is fuel economy.