A hybrid machine learning model for forecasting a billing period’s peak electric load days

Many models have been studied for forecasting the peak electric load, but studies focusing on forecasting peak electric load days for a billing period are scarce. This focus is highly relevant to consumers, as their electricity costs are determined based not only on total consumption, but also on the peak load required during a period. Forecasting these peak days accurately allows demand response actions to be planned and executed efficiently in order to mitigate these peaks and their associated costs. We propose a hybrid model based on ARIMA, logistic regression and artificial neural networks models. This hybrid model evaluates the individual results of these statistical and machine learning models in order to forecast whether a given day will be a peak load day for the billing period. The proposed model predicted 70% (40/57) of actual peak load days accurately and revealed potential savings of approximately USD $80,000 for an American university during a one-year testing period.     

多层级视角下京津冀科技创新政策府际关系研究

府际关系协调有利于实现创新资源、要素的有效汇聚与整合，进而提升政策有效性。以京津冀为例，构建中央-地方多层级府际关系模型，分析中央政策主体合作网络特征与政策扩散特征、地方政策主体合作网络特征与政策执行特征的相关性。研究发现，中央政府部门间形成了以科技部为核心的核心—边缘网络结构特征；北京市政府部门间呈现出以中关村科技管理委员会为核心的核心-边缘网络结构特征；天津市政府部门间呈现出以天津市科学技术委员会为核心的星型网络结构特征；河北省则没有形成稳定的网络关系。中央政策主体合作网络中的度中心性与政策扩散广度具有相关性；地方政策主体合作网络中介中心性、度中心性与政策执行强度具有相关性。     

Observing viewers’ self-reported and neurophysiological responses to message appeal in social media advertisements

PurposeMarketing research mainly uses self-reported method to record respondents' perceptions of creativity, and while self-reported method has its own merits, there exists some critique, particularly in terms of its ability to adequately capture the influence of message appeal on creativity. This paper studies how viewers’ responses to message appeals in social media advertisement compare in terms of self-reported responses versus responses taken through a neurophysiological method of Electroencephalograph (EEG).MethodologyTwo social media advertisements are displayed through a laboratory experiment to 17 subjects observing the subjects' neurophysiological reactions as well as their self-reported responses with regard to the commercials’ emotional, informational, and brand-related content.FindingsResults show that neurophysiological method offers unique details about emotional appeal, which the self-reported method fails to reflect. Furthermore, the neurophysiological measure identifies differences across the two target commercials in the emotional content part, which again are not identified through the self-reported method.OriginalityThis paper advances advertising research in social media literature by comparing content evaluation within advertisement through neurophysiological and self-reported measure. These findings have implications for marketers to use and measure message appeals in advertisement on social media to influence consumer response.     

An empirical investigation of water consumption forecasting methods

Many regions on earth face daily limitations in the quantity and quality of the water resources available. As a result, it is necessary to implement reliable methodologies for water consumption forecasting that will enable the better management and planning of water resources. This research analyses, for the first time, a large database containing data from 2 million water meters in 274 unique postal codes, in one of the most densely populated areas of Europe, which faces issues of droughts and overconsumption in the hot summer months. Using the R programming language, we built and tested three alternative forecasting methodologies, employing univariate forecasting techniques including a machine-learning algorithm, with very promising results.     

Predicting bank insolvencies using machine learning techniques

Proactively monitoring and assessing the economic health of financial institutions has always been the cornerstone of supervisory authorities. In this work, we employ a series of modeling techniques to predict bank insolvencies on a sample of US-based financial institutions. Our empirical results indicate that the method of Random Forests (RF) has a superior out-of-sample and out-of-time predictive performance, with Neural Networks also performing almost equally well as RF in out-of-time samples. These conclusions are drawn not only by comparison with broadly used bank failure models, such as Logistic, but also by comparison with other advanced machine learning techniques. Furthermore, our results illustrate that in the CAMELS evaluation framework, metrics related to earnings and capital constitute the factors with higher marginal contribution to the prediction of bank failures. Finally, we assess the generalization of our model by providing a case study to a sample of major European banks.     

Modelling the iron ore price index: A new perspective from a hybrid data reconstructed EEMD-GORU model

As iron ore is the fundamental steel production resource, predicting its price is strategically important for risk management at related enterprises and projects. Based on a signal decomposition technology and an artificial neural network, this paper proposes a hybrid EEMD-GORU model and a novel data reconstruction method to explore the price risk and fluctuation correlations between China’s iron ore futures and spot markets, and to forecast the price index series of China’s and international iron ore spot markets from the futures market. The analysis found that the iron ore futures market in China better reflected the price fluctuations and risk factors in the imported and international iron ore spot markets. However, the forward price in China’s iron ore futures market was unable to adequately reflect the changes in the domestic iron ore market, and was therefore unable to fully disseminate domestic iron ore market information. The proposed model was found to provide better market risk perceptions and predictions through its combinations of the different volatility information in futures and spot markets. The results are valuable references for the early-warning and management of the related enterprise project risks.     

基于卷积神经网络的列车检测算法研究

列车检测作为列车自动驾驶的核心技术,可以有效地降低列车追尾等事故造成的人身危险和财产损失。为实现精准的列车检测,选用改进的卷积神经网络(PVANET)对输入图像进行特征提取,在此基础上,采用候选区域网络,从生成的特征图里滑动搜索,判断出图像中可能为列车的区域位置,并进一步采用快速区域卷积神经网络对每个候选区域进行分类,计算出其所属类别的置信度,同时精确定位列车。经验证,该方法适应范围广、鲁棒性高,可以有效地检测不同环境光强及不同朝向的列车,保障列车安全,为列车自动驾驶及辅助驾驶提供安全保障。     

Co‐movements in commodity prices: a note based on network analysis

This article analyses co‐movements in a wide group of commodity prices during the time period 1992–2010. Our methodological approach is based on the correlation matrix and the networks inside. Through this approach we are able to summarize global interaction and interdependence, capturing the existing heterogeneity in the degrees of synchronization between commodity prices. Our results produce two main findings: (a) we do not observe a persistent increase in the degree of co‐movement of the commodity prices in our time sample, however from mid‐2008 to the end of 2009 co‐movements almost doubled when compared with the average correlation; (b) we observe three groups of commodities which have exhibited similar price dynamics (metals, oil and grains, and oilseeds) and which have increased their degree of co‐movement during the sampled period.     

Electricity price forecasting: A review of the state-of-the-art with a look into the future

A variety of methods and ideas have been tried for electricity price forecasting (EPF) over the last 15 years, with varying degrees of success. This review article aims to explain the complexity of available solutions, their strengths and weaknesses, and the opportunities and threats that the forecasting tools offer or that may be encountered. The paper also looks ahead and speculates on the directions EPF will or should take in the next decade or so. In particular, it postulates the need for objective comparative EPF studies involving (i) the same datasets, (ii) the same robust error evaluation procedures, and (iii) statistical testing of the significance of one model’s outperformance of another.     

The emergence of multi-sided platform MNEs: Internalization theory and networks

The rise of the digital economy provides firms across the globe with unique business opportunities. Companies such as Facebook, Alibaba, and Uber are competing in a new multi-sided platform world; the primary focus of these firms, from their inception, is to provide digital infrastructure, information and technology—intangible assets that enable direct interaction or value creation across platforms by linking different user group and complementors, often at the international level. Building on data drawn from multinational multisided Platform corporations (MMPCs) operating in China, we combine insights from internalization theory and network effects in understanding the value creation of such firms. We explore the boundaries of these new “breed” of MNEs in exploiting firm-specific advantages (FSAs) and in creating new knowledge between headquarters and subsidiaries. The findings suggest that internalization theory needs to shift its focus from the ‘boundaries of the firm’ to the ‘boundaries of the local network’. By integrating their internal and external networks of knowledge in adapting their business models in host markets, this new breed of MNEs is more likely than the traditional one to gain a sustainable competitive advantage in the new information age.