Insufficient Historical Data Research Articles

A novel clustering based algorithm to mitigate the demand of forecasting errors for newly deployed LTE cells with insufficient historical data

This study presents a novel clustering-based algorithm to mitigate the demand of forecasting errors of newly deployed LTE (Long-Term Evolution) cells with insufficient historical data. The numbers and the usage of mobile networks are growing day by day. So, new base stations are set every day, and the newly deployed cells do not have enough historical data to forecast. We developed a clustering-based algorithm to overcome this problem. We compared our approach with different forecasting methods such as classical time series methods, time series decomposition-based methods, and deep NN (Neural Network) methods. We tested our clustering-based solution compared with other approaches using seventy LTE cells’ daily historical performance data for two years. We collected this data from a Tier-1 Mobile Network Operator (MNO). We also analyzed the clustering features and benchmarked them for their contribution to the solution, and we measured the error rate by MAPE (Mean Absolute Percentage Error). As a result, we decreased the previous forecasting error rate from 133% to approximately 35%, showing that our novel algorithm is an efficient tool for this process.

ANN Hybrid Model for Forecasting Landfill Waste Potential in Lithuania

Waste management is currently a fast-growing environmental business and one of solutions to manage the huge amount of waste being generated on landfills is to use the disposed waste as an energy source. There is a major focus on energy forecasting, highlighting the importance of having reliable data on the volume and composition of municipal solid waste in landfills. However, the lack of historical data is forcing the development of machine-learning based models. This study contributes to this field by proposing a hybrid ANN-based model to forecast the total amount of landfill waste, different waste fraction and the potential for energy recovery. The proposed model includes an adaptive number of inputs adjusted to the relevant waste fraction and to the specific landfill. The obtained results substantiated that the proposed model allows for stable and accurate forecasting of recovered energy potential in cases where there is insufficient historical data. The experiments showed that the model with 12 inputs (meaning the forecast of the future value takes into account the last 12 months of data) was the most accurate in the energy forecasting task, with the lowest forecasting error in terms of mean absolute error −8.9878 gigawatt hours per year.

Spatial Electric Load Forecasting Method Based on High-Level Encoding of High-Resolution Remote Sensing Images

Spatial load forecasting (SLF) is important for regional power infrastructure construction planning and power grid management. However, for rapidly developing urban regions, SLF is generally inaccurate due to insufficient historical data. Hence, it is important to introduce the spatial load density (SLD) from similar regions to improve the accuracy of SLF. To select similar regions appropriately and acquire SLDs with limited available auxiliary data, this study proposes a spatial electric load forecasting method based on the high-level encoding of high-resolution remote sensing images called SELF-HE. In particular, SELF-HE introduces high-level ground object features as a key index to describe the characteristics of electric loads in a region and can establish connections between the remote sensing image features and SLD similarity. Based on this functionality, SELF-HE achieves more accurate SLF in regions with insufficient historical data. In the experiments, SELF-HE was compared with four traditional methods, and the results revealed that SELF-HE achieved improved SLF accuracy. Given that the high-resolution remote sensing images fully covered urban areas and were readily obtained, the proposed method can improve the accuracy of SLF with extremely low data collection costs and is applicable to rapidly developing urban regions.

Mixed Aleatory-epistemic Uncertainty Modeling of Wind Power Forecast Errors in Operation Reliability Evaluation of Power Systems

As the share of wind power in power systems continues to increase, the limited predictability of wind power generation brings serious potential risks to power system reliability. Previous research works have generally described the uncertainty of wind power forecast errors (WPFEs) based on normal distribution or other standard distribution models, which only characterize the aleatory uncertainty. In fact, epistemic uncertainty in WPFE modeling due to limited data and knowledge should also be addressed. This paper proposes a multi-source information fusion method (MSIFM) to quantify WPFEs when considering both aleatory and epistemic uncertainties. An extended focal element (EFE) selection method based on the adequacy of historical data is developed to consider the characteristics of WPFEs. Two supplementary expert information sources are modeled to improve the accuracy in the case of insufficient historical data. An operation reliability evaluation technique is also developed considering the proposed WPFE model. Finally, a double-layer Monte Carlo simulation method is introduced to generate a time-series output of the wind power. The effectiveness and accuracy of the proposed MSIFM are demonstrated through simulation results.

Automated Analysis of the US Drought Monitor Maps With Machine Learning and Multiple Drought Indicators.

The US Drought Monitor (USDM) is a hallmark in real time drought monitoring and assessment as it was developed by multiple agencies to provide an accurate and timely assessment of drought conditions in the US on a weekly basis. The map is built based on multiple physical indicators as well as reported observations from local contributors before human analysts combine the information and produce the drought map using their best judgement. Since human subjectivity is included in the production of the USDM maps, it is not an entirely clear quantitative procedure for other entities to reproduce the maps. In this study, we developed a framework to automatically generate the maps through a machine learning approach by predicting the drought categories across the domain of study. A persistence model served as the baseline model for comparison in the framework. Three machine learning algorithms, logistic regression, random forests, and support vector machines, with four different groups of input data, which formed an overall of 12 different configurations, were used for the prediction of drought categories. Finally, all the configurations were evaluated against the baseline model to select the best performing option. The results showed that our proposed framework could reproduce the drought maps to a near-perfect level with the support vector machines algorithm and the group 4 data. The rest of the findings of this study can be highlighted as: 1) employing the past week drought data as a predictor in the models played an important role in achieving high prediction scores, 2) the nonlinear models, random forest, and support vector machines had a better overall performance compared to the logistic regression models, and 3) with borrowing the neighboring grid cells information, we could compensate the lack of training data in the grid cells with insufficient historical USDM data particularly for extreme and exceptional drought conditions.

Nursing rescheduling problem with multiple rescheduling methods under uncertainty

The nursing rescheduling problem is a challenging decision-making task in hospitals. However, this decision-making needs to be made in a stochastic setting to meet uncertain demand with insufficient historical data or inaccurate forecasting methods. In this study, a stochastic programming model and a distributionally robust model are developed for the nurse rescheduling problem with multiple rescheduling methods under uncertain demands. We show that these models can be reformulated into an integer program. To illustrate the applicability and validity of the proposed model, a study case is conducted on three joint hospitals in Chengdu, Chongzhou, and Guanghan, Sichuan Province. The results show that the stochastic programming model and the distributionally robust model can reduce the cost by 78.71% and 38.92%, respectively. We also evaluate the benefit of the distributionally robust model against the stochastic model and perform sensitivity analysis on important model parameters to derive some meaningful managerial insights.

Decentralized Distributionally Robust Coordinated Dispatch of Multiarea Power Systems Considering Voltage Security

To achieve an efficient coordination of multiarea power systems, this article proposes a new coordinated dispatch optimization model for multiarea power systems. In the proposed optimization model, active and reactive powers are jointly optimized in a decentralized manner. Both of conventional generators and wind farms adopt the automatic voltage control strategy to mitigate the risk of undervoltage or overvoltage, and, thus, the nodal voltage security can be ensured in the optimization. At the same time, optimal admissible regions of wind power can be also obtained from decisions, which are optimized by making a tradeoff between the operational cost and renewable utilization in the optimization. Moreover, considering that it is hard to exactly estimate the true probability distribution of wind power due to modeling errors and insufficient historical data, the distributionally robust optimization is also employed in the proposed optimization model to take the uncertainty of wind power probability distributions into account. Besides, to improve the computational efficiency, an efficient algorithm is also developed. Numerical results on two test power systems demonstrate that compared with the traditional optimization models, the proposed decentralized dispatch model can achieve more reliable decisions and promote the renewable utilization.

Process system failure evaluation method based on a Noisy-OR gate intuitionistic fuzzy Bayesian network in an uncertain environment

In the system reliability evaluation of the process industries, it is sometimes difficult to get precise and sufficient failure data of system components utilized to calculate the failure probability. In this study, a Noisy-OR gate Bayesian network method based on intuitionistic fuzzy theory is proposed in cases of imprecise and insufficient historical data. The main contributes of this method include: a set of triangular intuitionistic fuzzy numbers considering uncertainty and hesitation is defined based on the standards and industry practices, meanwhile, a corresponding probability conversion method is also proposed; an improved similarity aggregation method is employed for less uncertainty accumulation and reducing the deviation caused by individual differences during the aggregation; the uncertain causal relationship among the relevant nodes is determined by applying the Noisy-OR gate in the Bayesian network. Furthermore, a case study of the crude oil tank fire and explosion accident is performed to illustrate the applicability of proposed approach. The comparison between the obtained results and that from pre-existing methods shows that the proposed method can provide a more suitable result in an uncertain environment. The weak links of the crude oil tank system are identified through Bayesian reasoning and sensitivity analysis, which can aid decision-making and improve the security execution of the crude oil tank system.

A Novel Scenario Generation Framework Based on the Knowledge of Existing Wind Power Plants

As a kind of renewable energy with economic and environmental friendliness, wind energy is widely used, although the high uncertainty of wind speed has significant impact on the operation and planning of wind power systems. Thus, scenario generation of wind speed is the primary step to obtain optimal decisions. However, as to the wind power plants to be newly-built or expanded, adequate wind data may not be available, or even the wind data are missing or invalid, which may lead to the inaccuracy of data-driven scenario generation. In this paper, considering that multiple wind plants in neighboring areas may have similar wind patterns, a novel scenario generation framework is proposed to transfer the knowledge extracted from existing data-rich plants to help establish the uncertainty model of newly-built plant. Transfer component analysis (TCA) is used to minimize the distribution gap between multiple source plants and target plant, and the results of maximum mean discrepancy (MMD) can be obtained to construct the mixture distribution model of target wind speed. Experimental results show that the scenarios generated by transferring the knowledge of existing wind plants can better reflect the real characteristics of the target wind speed in the case of insufficient historical data.

Construction and Empirical Research on the Dynamic Provisioning Model of China’s Banking Sector under the Macro-Prudential Framework

Since the international financial crisis in 2008, to achieve the political goal of financial stability, academic circles, financial industry, and regulatory authorities worldwide have deeply reflected on the current economic regulatory theories and policy adjustment tools through introducing the macroprudential policy. The dynamic provisioning system is a counter-cyclical policy tool in the macro-prudential adjustment framework widely used in the world. This paper uses the binary Gaussian Copula function to combine the measurement method of the default distance in the contingent claims analysis method with the risk warning idea based on the Probit model and proposes the contingent claims analysis (CCA)–Probit–Copula dynamic provisioning model based on nine forward-looking indicators. Based on China’s actual conditions, this model solves present problems faced by the current dynamic provisioning system in China, such as insufficient historical credit data reserves of commercial banks, excessive reliance on subjective judgments, and conflicts with the current accounting system. Moreover, this model can put forward corresponding counter-cyclical provisioning requirements according to the influence degree of macro-cyclical factors to different commercial banks’ own default risk, which not only takes into account the security and liquidity of commercial banks, but also ensures their profitability and competitiveness. Based on the empirical test of historical data from listed commercial banks in China, it proves that the dynamic provisioning requirements proposed in this model can effectively adjust the overall credit scale of the banking industry in counter-cyclical ways, thereby achieving the policy goals of counter-cyclical adjustment under the macro-prudential framework and maintaining the security of China’s financial system and the sustainable development of the macroeconomy.

Evaluation and comparison of the results of the NET-VISA seismic event association method based on Bayesian theory

Seismic monitoring is an important verification technique under the Comprehensive Nuclear-Test-Ban Treaty. Phase association technology, which is an important component of seismic data processing, associates signals generated from the same event source recorded at multiple stations and determines event information based on signal features. Seismic event association based on the historical seismic data feature model is a research hot spot in the field of seismic monitoring. In this paper, an event association method called NET-VISA based on Bayesian theory is introduced; then, the application of the historical data feature model in NET-VISA is analyzed. The NET-VISA method is evaluated using the International Data Centre LEB bulletins published by the Comprehensive Nuclear-Test-Ban Treaty Organization, the ISC Reviewed Bulletins, and the China Earthquake Networks Center bulletin as reference sets. The results show that for the global sparse network, NET-VISA is generally superior to the GA method currently used by the IDC, which verifies NET-VISA's effectiveness. However, NET-VISA misses some events detected by the GA. The reasons might be that these events are located in regions with low seismic activity and that insufficient historical event data exists, resulting in unreasonable scoring results.Finally, the application method and research direction of NET-VISA in actual scenarios are discussed.

Risk assessment models to investigate the impact of emergency on a water supply system

Abstract A water supply system is a critical infrastructure to support industrial and agricultural production and human life. It often operates abnormally in an emergent risk situation, resulting in shortage or suspension of water supply, even health risk and economic losses. In order to reduce negative impacts posed by these potential threats, identifying and evaluating possible risks in a water supply system has been becoming more necessary. For this reason, we establish two risk assessment models in accordance with two different situations based on uncertainty theory in the presence of insufficient historical data. In the proposed models, we first discuss emergency in three respects: the possibility of emergency occurrence, the consequence caused by emergency and system vulnerability. Then the risk to a water supply system is defined by the uncertain measure of loss-positive by incorporating a risk tolerance index and loss function contributed by the above analysis. Moreover, several theorems for calculating the risk index of series and parallel water supply systems are presented. Finally, we illustrate the feasibility and validity of the proposed models by implementing a series of numerical examples and further present some noteworthy observations.

Algorithms for mitigating the effect of uncertain geomagnetic disturbances in electric grids

Geomagnetic disturbances (GMDs), a result of space weather, pose a severe risk to electric grids. When GMDs occur, they can cause geomagnetically-induced currents (GICs), which saturate transformers, induce hot-spot heating, and increase reactive power losses in the transmission grid. Furthermore, uncertainty in the magnitude and orientation of the geo-electric field, and insufficient historical data make the problem of mitigating the effects of uncertain GMDs challenging. In this paper, we propose a novel distributionally robust optimization (DRO) approach that models uncertain GMDs and mitigates the effects of GICs on electric grids. This is achieved via a set of mitigation actions (e.g., line switching, locating blocking devices, generator re-dispatch and load shedding), prior to the GMD event, such that the worst-case expectation of the system cost is minimized. To this end, we develop a column-and-constraint generation algorithm that solves a sequence of mixed-integer second-order conic programs to handle the underlying convex support set of the uncertain GMDs. Also, we present a monolithic exact reformulation of our DRO model when the underlying support set can be approximated by a polytope with three extreme points. Numerical experiments on ‘epri-21’ system show the efficacy of the proposed algorithms and the exact reformulation of our DRO model.

Results and lessons from local calibration process of the Highway Safety Manual for the state of Maryland: Freeway segments, speed-change lanes, and ramp terminals

In 2014, the authors successfully developed local calibration factors (LCFs) for the rural and urban arterials and highways for the State of Maryland. In this research, the authors computed LCFs for freeway segments, speed-change lanes, and signalized and stop-controlled ramp terminals to complement and complete LCFs for Maryland to adjust predicted motor vehicle crashes as part of the Highway Safety Manual (HSM) application. Implementing the HSM requires local calibration or development of jurisdiction-specific models. Either one is a laborious and challenging process because, despite being explained in the manual, the process is not easy or straightforward. Thus, the main contribution of this article is providing a detailed case study of the most complete calibration effort with the HSM Supplement models. Furthermore, the current state of the HSM Supplement adoption by different states is discussed. The results of the conducted research are summarized including the LCF development procedure and challenges that the authors had to overcome as well as some suggestions. LCFs were computed for data from a three-year study period (2008–2010) from multiple resources of Maryland government agencies. Ramps and collector-distributor (C-D) roads were excluded due to insufficient historical crash data. Baltimore City was also excluded because of a different data collection scheme.

A Soft-Path Solution to Risk Reduction by Modeling Medical Waste Disposal Center Location-Allocation Optimization.

The risk of medical waste pollution and huge demand of daily medical waste disposal pose great difficulties to medical waste management. Establishing medical waste disposal centers (MWDCs) is considered one of the ways to reduce the environmental and public risk of medical waste pollution. However, how to serve the medical waste disposal demand in optimal MWDCs' locations is a key challenge due to the complexity of the whole system and relationships among stakeholders. This article develops a soft-path solution for reducing risks as well as mitigating the related costs by optimizing the MWDC location-allocation problem. A risk mitigation-oriented bilevel equilibrium optimization model is developed for modeling the Stackelberg game behavior between the local government and the medical institutions. The objectives of the local government are minimizing the total risk of loss, the subsidy costs, and the investment cost of building the MWDCs, while minimizing the disposal and transportation costs are the objectives at the medical institution level. Fuzzy random variables are introduced by combining insufficient historical data with expert knowledge via consulting surveys to describe the coexisting uncertainties in the data. To solve the model, a hybrid approach combined with the interactive fuzzy programming technique and an Entropy-Boltzmann selection-based genetic algorithm are designed and tested. The Chengdu Medical Waste Disposal Centers Planning Project is used as a practical application. The results show that it is possible to achieve a balanced market with higher economic efficiency and significantly reduced risk through an appropriate principle of interactive actions between the bilevel stakeholders.

A New Approach for Short-term Time Series Forecasting

Short-term series forecasting is one of the essential issues in a variety of tasks, such as traffic flow prediction, stock market tendency analysis, etc. Most current methods based on stable or abundant historical data. In this paper, we proposed a novel model called LA-NN. It takes advantages of both long short-term memory (LSTM) network and autoregressive integrated moving average (ARIMA) by a relation integration of them. So as to deal with the situation of insufficient historical data and sudden abnormal changes in data. A comparison with other representative forecast models validates that the proposed LA-NN network can achieve a better performance.

A Fuzzy-Based Risk Assessment Methodology for Construction Projects Under Epistemic Uncertainty

In this paper, a methodology for construction projects risk assessment under epistemic uncertainty (i.e., uncertainty arising from lack of data/knowledge) has been proposed. In practice, as the sufficient data from historical sources for probabilistic analysis are quite difficult to obtain, qualitative risk assessment methodologies based on expert’s judgments are commonly used in construction industry. However, these insufficient probabilistic data combined with experts’ judgments can be used in the risks evaluation process to reduce uncertainties and biasness. All the methodologies developed so far have assumed that the degrees of uncertainties (i.e., levels of uncertainties) involved in individual risk event are equal. However, in practice, the degree of uncertainties that involved in each risk event may vary due to the variation in the availability or quality of data obtained from multiple sources (e.g., from experts’ opinions and past data from similar projects). Therefore, evaluation of risks considering the degree of uncertainty involved in individual risk events may assist project manager in setting-up response strategies to mitigate threat to the project objectives. This paper proposes a risk assessment methodology using triangular fuzzy numbering system to compute risk value by combining expert’s opinion and insufficient historical data. A modified form of general ramp-type fuzzy membership function for quantification of uncertainty range of each risk event and an extended VIKOR method for risk ranking with these uncertainty ranges have been used. The most notable difference with other fuzzy risk assessment methods is the use of algorithm to handle the uncertainties involved in individual risk event. The proposed risk assessment methodology is illustrated for two practical example problems: (1) a steel-frame structured building and (2) a rehabilitation project of a building.

Revival of mature onshore oil field in U.S.

During planning stage of CO2 EOR （enhanced oil recovery）, evaluation of remaining oil volume is one of the most important works because of its significant impact on field performance and project economics. However, the evaluation becomes very challenging when the concerned field is a legacy field where historical data are insufficient unless reliable data were later supplemented. We describe how to evaluate CO2 EOR project for a legacy field with insufficient historical data.

A Multi-Objective Portfolio Selection Model With Fuzzy Value-at-Risk Ratio

Considering nonstatistical uncertainties and/or insufficient historical data in security return forecasts, fuzzy set theory has been applied in the past decades to build portfolio selection models. Meanwhile, various risk measurements such as variance, entropy, and Value-at-Risk have been proposed in fuzzy environments to evaluate investment risks from different perspectives. Sharpe ratio, also known as the reward-to-variability ratio, which measures the risk premium per unit of the nonsystematic risk (asset deviation), has received great attention in modern portfolio theory. In this study, the Sharpe ratio in fuzzy environments is introduced, whereafter, a fuzzy Value-at-Risk ratio is proposed. Compared with Sharpe ratio, Value-at-Risk ratio is an index with dimensional knowledge that reflects the risk premium per unit of the systematic risk (the greatest loss under a given confidence level). On the basis of the two ratios, a multi-objective model is built to evaluate their joint impact on portfolio selection. Then, the proposed model is solved by a fuzzy simulation based multi-objective particle swarm optimization algorithm, where the global best of each iteration is determined by an improved dominance times based method. Finally, the algorithm superiority is justified via comparing with existing solvers on benchmark problems, and the model effectiveness is exemplified by using three case studies on portfolio selection.

ANALYSIS OF THE CAUSES AND LEVEL OF MAINTENANCE FOR ENTERPRISE SYSTEMS IN CONSTRUCTION COMPANIES

A construction company without a similar information technology (IT) system in the past has insufficient historical data to use for investment decision-making of IT system. An estimation of maintenance costs is especially more uncertain than the initial investment costs, and the uncertainty is greater when the IT system is used over a long period, such as an enterprise system (ES). This study proposes estimation criteria for the maintenance costs of an ES for an accurate investment decision. First, the causes of maintenance are determined, and the level of maintenance analyzed. Then, the result is compared with a general trend of maintenance incidence (bathtub curve) that is widely used as reference criteria to estimate maintenance cost. The level of maintenance was high during the early stage but steadily decreased in the middle and end stage because high-cost maintenance activities were postponed with the approach of the time in which the ES was being restructured. This trend is different from the bathtub curve that increases again during the end stage. Thus, when a maintenance contract is negotiated, the maintenance level that affects maintenance cost should be considered as well as the incidence of maintenance.