Benchmarking robustness of load forecasting models under data integrity attacks

As the internet’s footprint continues to expand, cybersecurity is becoming a major concern for both governments and the private sector. One such cybersecurity issue relates to data integrity attacks. This paper focuses on the power industry, where the forecasting processes rely heavily on the quality of the data. Data integrity attacks are expected to harm the performances of forecasting systems, which will have a major impact on both the financial bottom line of power companies and the resilience of power grids. This paper reveals the effect of data integrity attacks on the accuracy of four representative load forecasting models (multiple linear regression, support vector regression, artificial neural networks, and fuzzy interaction regression). We begin by simulating some data integrity attacks through the random injection of some multipliers that follow a normal or uniform distribution into the load series. Then, the four aforementioned load forecasting models are used to generate one-year-ahead ex post point forecasts in order to provide a comparison of their forecast errors. The results show that the support vector regression model is most robust, followed closely by the multiple linear regression model, while the fuzzy interaction regression model is the least robust of the four. Nevertheless, all four models fail to provide satisfying forecasts when the scale of the data integrity attacks becomes large. This presents a serious challenge to both load forecasters and the broader forecasting community: the generation of accurate forecasts under data integrity attacks. We construct our case study using the publicly-available data from Global Energy Forecasting Competition 2012. At the end, we also offer an overview of potential research topics for future studies.     

A Mathematical Programming Approach for Measuring Technical Efficiency in a Fuzzy Environment

A three stage approach is proposed to measure technical efficiency in a fuzzy environment. This approach uses the traditional data envelopment analysis framework and then merges concepts developed in fuzzy parametric programming (Carlsson and Korhonen, 1986). In the first stage, vague input and output variables are expressed in terms of their risk-free and impossible bounds and a membership function. This membership function represents the degree to which a production scenario is plausible. In the second stage, conventional efficiency measurement models (Banker, Charnes and Cooper, 1984; Deprins, Simar and Tulkens, 1984) are re-formulated in terms of the risk-free and impossible bounds and the membership function for each of the fuzzy input and output variables. In the third stage, technical efficiency scores are computed for different values of the membership function so as to identify uniquely sensitive decision making units. The approach is illustrated in the context of a preprint and packaging manufacturing line which inserts commercial pamphlets into newspapers.     

A fuzzy bi-level method for modeling age-specific migration

Modeling and forecasting international migration are significant research areas since migration forecasts are vital in decision making and policy design regarding economy, security, society, and resource allocation. The methods for modeling and forecasting migration rely on strict subjective or statistical assumptions which may not always be met. In addition, lack of a universally accepted definition of the term “migrant” and the ambiguities in data due to recording and collection systems result in inconsistencies and vagueness in migration modeling. Considering these, in this paper, a fuzzy bi-level age-specific migration modeling method is proposed. The bi-level structure embedded in the model makes use of the well-known Lee-Carter method as well as fuzzy regression, singular value decomposition technique, and hierarchical clustering to reflect the general characteristics of the country of concern together with the distinct emigration and immigration behaviors of the age groups. Bayesian time series models are fitted to the time-variant fuzzy parameters obtained through the proposed method to forecast future migration values. The proposed method is applied on female and male age-specific emigration and immigration counts of Finland for 1990–2010 period and Germany for 1995–2012 period, and the future values are forecasted for 2011–2025 and 2013–2025 respectively. The method is compared with an existing Bayesian approach and the numerical findings display that the proposed fuzzy method is superior to the existing one in modeling and forecasting age-specific migration values within significantly narrower prediction intervals.     

Intuitionistic fuzzy sets in questionnaire analysis

Fuzzy sets represent an extension of the concept of set, used to mathematically model veiled and indefinite concepts, such as those of youth, poverty, customer satisfaction and so on. Fuzzy theory introduces a membership function, expressing the degree of membership of the elements to a set. Intuitionistic fuzzy sets and hesitant fuzzy sets are two extensions of the theory of fuzzy sets, in which non-membership degrees and hesitations expressed by a set of experts are, respectively, introduced. In this paper, we apply intuitionistic fuzzy sets to questionnaire analysis, with a focus on the construction of membership, non-membership and uncertainty functions. We also suggest the possibility of considering intuitionistic hesitant fuzzy sets as a valuable theoretical framework. We apply these models to the evaluation of a Public Administration and we assess our results through a sensitivity analysis.     

基于粒子群算法的模糊插值建模方法研究

由于传统模糊建模方法中模型参数都是人们根据经验选取的,它对于不同系统的动态跟踪能力不同,泛化能力差。针对常规模糊推理的局限性,本文提出一种基于参数的模糊插值建模方法。设计了模糊糊理系统的模型和参数调整方案,利用粒子群算法优化模型中的参数,仿真结果表明该方法的有效性。     

基于模糊多准则决策方法的物流中心选址研究

提出了一种物流中心选址的模糊多准则决策算法,以降低决策者主观偏好及评估过程中模糊和不确定因素对选址决策的影响。算法可同时考虑质化与量化因素及因素间不同权重的影响。模糊估计值的隶属函数可迭代求出,再应用平均位移原理进行积分推导来求解模糊值以进行各候选位置的比序。最后,以应用实例对模型的有效性和可行性进行了验证。     

The accuracy of long-term earnings forecasts in the electric utility industry

This paper examines the accuracy of various methods of forecasting long-term earnings growth for firms in the electric utility industry. In addition to a number of extrapolative techniques, Value Line analyst forecasts are also evaluated. Value Line analyst forecasts for a five-year time horizon are found to be superior to many of the extrapolative models. Among the extrapolative models examined, implied growth and historical book value per share growth rate models performed best. These results provide strong support for using Value Line growth forecasts in cost of capital estimates for electric utilities in the context of utility rate cases. Value Line forecast errors could be explained by changes in dividend payout ratios, the firm's regulatory environment and bond rating changes.     

考虑需求不确定的订单分配模型研究

从顾客需求不确定性出发,在考虑批量折扣的情况下,构建了一个基于成本、延迟交货数量、质量和交货提前期的具有模糊权重的多目标订单分配模型。在求解过程中,用隶属度函数表示决策者对各目标值的满意程度,用梯形模糊数来表示各指标的权重,然后将多目标规划问题转化为求全局满意度最优的单目标规划问题。最后通过一个数值算例说明了模型的求解过程。     

Group-based judgmental forecasting: An integration of extant knowledge and the development of priorities for a new research agenda

We review and integrate the extant knowledge on group-based forecasting, paying particular attention to the papers included in this special issue of the International Journal of Forecasting. We focus on the relative merits of different methods of aggregating individual forecasts, the advantages of heterogeneity in group memberships, the impact of others’ opinions on group members, and the importance of perceptions of trust. We conclude that a change of opinion following group-based deliberation is most likely to be appropriate where the group membership is heterogeneous, the minority opinion is protected from pressure to conform, information exchange between group members has been facilitated, and the recipient of the advice is able — by reasoning processes — to evaluate the reasoning justifying the proffered advice. Proffered advice is least likely to be accepted where the advisor is not trusted — an evaluation which is based on the advisor having different perceived values to the recipient and being thought to be self-interested. In contrast, the outcome of a group-based deliberation is most likely to be accepted when there is perceived to be procedural fairness and the participants in the process are perceived to be trustworthy. Finally, we broaden our discussion of group-based forecasting to include a consideration of other group-based methodologies which are aimed at enhancing judgment and decision making. In particular, we discuss the relevance of research on small-group decision making, the nature and quality of the advice, group-based scenario planning, and public engagement processes. From this analysis, we conclude that, for medium- to long-term judgemental forecasting, a variety of non-outcome criteria need to be considered in the evaluation of alternative group-based methods.     

Heterogeneous expectations and debt in a growth model for a small open economy

We extend an aggregative growth model for a small open economy by developing a framework in which boundedly rational agents raise credit in proportion to their expected income. Moreover, these agents are heterogeneous in the sense that they switch between an extrapolative and a regressive forecasting rule with respect to perceived market circumstances. Using a mixture of analytical and numerical tools, we attempt to describe the characteristics of our model’s dynamical system. Our results then suggest that self-fulfilling short-run expectations do not only have important consequences for fluctuations in economic activity but are also a source of simple endogenous dynamics.     

基于模糊综合评判模型的软件非功能属性评估方法研究

文章将模糊综合评判模型应用于软件非功能属性的评估,提出一种单因素模糊评价方法——标尺法,以及基于模糊集重心概念将以模糊数表示的综合评价结果转换为明晰数的解模糊方法。与传统方法相比,评价者使用标尺法进行模糊评分,简单、直观,计算量小,更加易于实现,并且保持了一定的语义分辨度。     

A multiple linear regression model for imprecise information

In standard regression analysis the relationship between the (response) variable and a set of (explanatory) variables is investigated. In the classical framework the response is affected by probabilistic uncertainty (randomness) and, thus, treated as a random variable. However, the data can also be subjected to other kinds of uncertainty such as imprecision. A possible way to manage all of these uncertainties is represented by the concept of fuzzy random variable (FRV). The most common class of FRVs is the LR family (LR FRV), which allows us to express every FRV in terms of three random variables, namely, the center, the left spread and the right spread. In this work, limiting our attention to the LR FRV class, we consider the linear regression problem in the presence of one or more imprecise random elements. The procedure for estimating the model parameters and the determination coefficient are discussed and the hypothesis testing problem is addressed following a bootstrap approach. Furthermore, in order to illustrate how the proposed model works in practice, the results of a real-life example are given together with a comparison with those obtained by applying classical regression analysis.     

Developing a multivariate approach to monitor fuzzy quality profiles

Referring to several applications in which the response quality characteristic is fuzzy, this paper studies how the profile functional relationship between a fuzzy response variable and a predictor variable can be monitored by using a fuzzy regression model which is referred to as profile. The purpose of this paper is to develop a multivariate approach for monitoring process/product fuzzy quality profiles in phase I for applications where the quality characteristic is linguistic, imprecise, vague or deficient. The multivariate approach includes three fuzzy multivariate control charts which are developed by using fuzzy set theory to monitor fuzzy profiles in order to achieve an in-control process. The performance of developed approach is investigated on the basis of signal probability in various out-of-control scenarios through a simulation study. Compared with univariate approach, the results indicate a good performance of our multivariate approach in detecting all sized shifts in process profiles. A real case in tourism industry is utilized to show the applicability of the proposed approach.     

Measuring process capability index <Emphasis Type="Italic">C</Emphasis><Subscript><Emphasis Type="Italic">pm</Emphasis></Subscript> with fuzzy data

The process capability index C _pm , which considers the process variance and departure of the process mean from the target value, is important in the manufacturing industry to measure process potential and performance. This paper extends its applications to calculate the process capability index [(C)\tilde]_pm{\tilde {C}_{pm} } of fuzzy numbers. In this paper, the α-cuts of fuzzy observations are first derived based on various values of α. The membership function of fuzzy process capability index [(C)\tilde]_pm{\tilde {C}_{pm} } is then constructed based on the α-cuts of fuzzy observations. An example is presented to demonstrate how the fuzzy process capability index [(C)\tilde]_pm{\tilde {C}_{pm} } is interpreted. When the quality characteristic cannot be precisely determined, the proposed method provides the most possible value and spread of fuzzy process capability index [(C)\tilde]_pm{\tilde {C}_{pm} }. With crisp data, the proposed method reduces to the classical method of process capability index C _pm .     

Enhancing capacity planning through forecasting: An integrated tool for maintenance of complex product systems

Despite the extensive amount of data generated and stored during the maintenance capacity planning process, Maintenance, Repair, and Overhaul (MRO) organizations have yet to explore their full potential in forecasting the required capacity to face future and unprecedented maintenance interventions. This paper explores the integration of time series forecasting capabilities in a tool for maintenance capacity planning of complex product systems (CoPS), intended to value data that is routinely generated and stored, but often disregarded by MROs. State space formulations with multiplicative errors for the simple exponential smoothing (SES), Holt’s linear method (HLM), additive Holt-Winters (AHW), and multiplicative Holt-Winters (MHW) are assessed using real data, comprised of 171 maintenance projects collected from a major Portuguese aircraft MRO. A state space formulation of the MHW, selected using the bias-corrected Akaike information criterion (AICc), is integrated in a Decision Support System (DSS) for capacity planning with probabilistic inference capabilities and used to forecast the workload probability distribution of a future and unprecedent maintenance intervention. The developed tool is validated by comparing forecasted values with workloads of a particular maintenance intervention and with a model simulating current forecasting practices employed by MROs.     

An integrated fuzzy mathematical model and principal component analysis algorithm for forecasting uncertain trends of electricity consumption

This paper introduces an integrated algorithm for forecasting electricity consumption (EL) based on fuzzy regression, time series and principal component analysis (PCA) in uncertain markets such as Iran. The algorithm is examined by mean absolute percentage error, analysis of variance (ANOVA) and Duncan Multiple Range Test. PCA is used to identify the input variables for the fuzzy regression and time series models. Monthly EL in Iran is used to show the superiority of the algorithm. Moreover, it is shown that the selected fuzzy regression model has better estimated values for total EL than time series. The algorithm provides as good results as intelligent methods. However, it is shown that the algorithm does not require utilization of preprocessing methods but genetic algorithm, artificial neural network and fuzzy inference system require preprocessing which could be a cumbersome task to deal with ambiguous data. The unique features of the proposed algorithm are three fold. First, two type of fuzzy regressions with and without preprocessed data are prescribed by the algorithm in order to minimize the bias. Second, it uses PCA approach instead of trial and error method for selecting the most important input variables. Third, ANOVA is used to statistically compare fuzzy regression and time series with actual data.     

Crude oil price forecasting incorporating news text

Sparse and short news headlines can be arbitrary, noisy, and ambiguous, making it difficult for classic topic model LDA (latent Dirichlet allocation) designed for accommodating long text to discover knowledge from them. Nonetheless, some of the existing research about text-based crude oil forecasting employs LDA to explore topics from news headlines, resulting in a mismatch between the short text and the topic model and further affecting the forecasting performance. Exploiting advanced and appropriate methods to construct high-quality features from news headlines becomes crucial in crude oil forecasting. This paper introduces two novel indicators of topic and sentiment for the short and sparse text data to tackle this issue. Empirical experiments show that AdaBoost.RT with our proposed text indicators, with a more comprehensive view and characterization of the short and sparse text data, outperforms the other benchmarks. Another significant merit is that our method also yields good forecasting performance when applied to other futures commodities.     

基于模糊控制的冷库能量优化研究

为了减小冷库目前的能耗,提出采用模糊控制策略的能量优化策略,给出了基于自适应模糊控制的系统控制体系结构,制定了模糊推理规则和隶属度函数,使得制冷设备按照合理优化的方法运行,实验结果表明了所提能量优化策略的有效性和可行性。     

往复式空气压缩机模糊故障诊断

针对某煤矿空压机站往复式空压机故障诊断的要求,以模糊(Fuzzy)理论为依据,构建了一种往复式空压机模糊故障诊断模型。采用求距离的方法构造隶属度函数,求得待诊断对象对各个典型故障的隶属度,再由最大隶属度原则确定故障原因。该诊断模型可以根据往复式空压机的故障征兆进行有效的故障监测和诊断,可以方便的实现智能诊断。对煤矿上以及其它领域的大型设备状态监测和诊断也有重要参考价值。     

Experience curve models in the electricity supply industry

There has been much controversy over the use of the Experience Curve for forecasting purposes. The Experience Curve model has been criticised both on theoretical grounds and because of the practical problems of using it. An alternative model of experience effects due to Towill has certain attractions from the standpoint of theory. However, a rather deeper question is whether experience curve type models produce superior forecasts to those derived using extrapolative techniques.This paper examines these questions in the context of three time series taken from the electricity supply industry, viz: average thermal efficiency; works costs; and price of electricity. The two latter series require price deflation. Both the implied GDP consumption deflator, and a wholesale price index for fuel and electricity were used for this purpose. It is argued that because of the absence of substitutes and of the effects of competition, along with the high quality of data available on the electricity supply industry, these three series provide a favourable test of the experience curve approach to forecasting. The two experience curves performed on the whole markedly worse than the simpler extrapolative methods on the two financial series examined. For the average thermal efficiency series the Towill model and the Experience Curve model marginally outperformed the extrapolative methods.Overall, there was little support for using either the Experience Curve or Towill models. These are obviously more difficult to use than simple univariate models and do not provide significantly better forecasts. Moreover, the Towill model gave rise to considerable estimation and specification problems with the data used here.