Forecasting Transportation Infrastructure Impacts of Renewable Energy Industry Using Neural Networks

Abstract

Iowa is a state rich in renewable energy resources, especially biomass. The successful development of renewable energy industry in Iowa is concomitant with increase in freight traffic and is likely to have significant impacts on transportation infrastructure condition and increased maintenance expenses for the state and local governments. The primary goal of this paper is to investigate the feasibility of employing the Neural Networks (NN) methodology to forecast the impacts of Iowa's biofuels and wind power industries on Iowa's secondary and local road condition and maintenance-related costs in a panel data framework. The data for this study were obtained from a number of sources and for a total of 24 counties in clusters in Northern, Western, and Southern Iowa over a period of ten years. Back-Propagation NN (BPNN) using a Quasi-Newton secondorder training algorithm was chosen for this study owing to its very fast convergence properties. Since the size of the training set is relatively small, ensembles of well-trained NNs were formed to achieve significant improvements in generalization performance. The developed NN forecasting models could identify the presence of biofuel plants and wind farms as well as large-truck traffic as the most sensitive inputs influencing pavement condition and granular and blading maintenance costs. Pavement deterioration resulting from traffic loads was found to be associated with the presence of both biofuel plants and wind farms. The developed NN forecasting models can be useful in identifying and properly evaluating future transportation infrastructure impacts resulting from the renewable energy industry development and thus help Iowa maintain its competitive edge in the rapidly developing bioeconomy. keywords: neural networks, renewable energy, biofuel plants, wind farms, forecasting, panel data, infrastructure impacts.

Topological Structural Analysis of China's New Energy Stock Market: A Multi-Dimensional Data Network Perspective

“…Since traditional fossil energy sources are depletable and cause a lot of pollution problems during use, in the past period, new energy sources have become the target of vigorous development in various countries (Wang et al, 2018). The continuous maturity of the new energy industry has a great impact on infrastructure construction (Gopalakrishnan & Gkritza, 2014), economic growth (Soava et al, 2018;Popescu et al, 2019) and sustainable development (Aceleanu et al, 2018), which has attracted the continuous attention of market investors (Chang & Shieh, 2017). According to "Global Trends in Renewable Energy Investment" (Bloomberg New Energy Finance, 2018), since 2004, the cumulative investment in global renewable energy has reached 2.9 trillion US dollars, and the investment in 2017 was 279.8 billion US dollars, an increase of 15.8% compared with 2016.…”