Paradigm shift to enhanced water supply planning through augmented grids,scarcity pricing and adaptive factory water: A system dynamics approach

This paper details a system dynamics model developed to simulate proposed changes to water governance through the integration of supply, demand and asset management processes. To effectively accomplish this, interconnected feedback loops in tariff structures, demand levels and financing capacity are included in the model design, representing the first comprehensive life-cycle modelling of potable water systems. A number of scenarios were applied to Australia's populated South-east Queensland region, demonstrating that introducing temporary drought pricing (i.e. progressive water prices set inverse with availability), in conjunction with supply augmentation through rain-independent sources, is capable of efficiently providing water security in the future. Modelling demonstrated that this alternative tariff structure reduced demand in scarcity periods thereby preserving supply, whilst revenues are maintained to build new water supply infrastructure. In addition to exploring alternative tariffs, the potential benefits of using adaptive pressure-retarded osmosis desalination plants for both potable water and power generation was explored. This operation of these plants for power production, when they would otherwise be idle, shows promise in reducing their net energy and carbon footprints. Stakeholders in industry, government and academia were engaged in model development and validation. The constructed model displays how water resource systems can be reorganised to cope with systemic change and uncertainty.     

基于ARIMA时序辨识的需水量预测

供水行业是国民经济的重要基础,对需水量的准确预测有利于供水部门调度。针对城市供水量波动特点和预报要求,基于ARIMA季节时间序列对城市需水状况建模。通过分析自相关系数(ACF)、偏自相关系数(PACF)等参数辨识模型阶次结构,预报未来需水量趋势,并使用SAS统计软件进行检验。所建立的模型成功应用于上海市中心城区需水预报,对照历史数据表明,模型具有理想的预测精度,能够有效地辅助供水部门进行决策。     

Smart grid coordination in building HVAC systems: EMPC and the impact of forecasting

Energy consumption by heating, ventilation, and air conditioning (HVAC) systems exhibits a clear correlation with electricity prices. The method of economic model predictive control (EMPC) can be used in conjunction with thermal energy storage (TES) to time-shift power consumption away from periods of high demand to periods of low energy cost. Dynamic electricity pricing and weather condition forecasts can be readily incorporated within this methodology. Unfortunately, the receding horizon nature of this control strategy makes it very susceptible to the quality of the forecasts used. To this end, the development and implementation of several forecasting methods will be discussed. Finally, the EMPC performance of these methods will be assessed on a simple building example using active TES.     

Reliable water supply system design under uncertainty

Given the natural variability and uncertainties in long-term predictions, reliability is a critical design factor for water supply systems. However, the large scale of the problem and the correlated nature of the involved uncertainties result in models that are often intractable. In this paper, we consider a municipal water supply system over a 15-year planning period with initial infrastructure and possibility of construction and expansion during the first and sixth year on the planning horizon. Correlated uncertainties in water demand and supply are applied on the form of the robust optimization approach of Bertsimas and Sim to design a reliable water supply system. Robust optimization aims to find a solution that remains feasible under data uncertainty. Such a system can be too conservative and costly. In the Bertsimas and Sim approach, it is possible to vary the degree of conservatism to allow for a decision maker to understand the tradeoff between system reliability and economic feasibility/cost. The degree of conservatism is incorporated in the probability bound for constraint violation. As a result, the total cost increases as the degree of conservatism (and reliability) is increased. In the water supply system application, a tradeoff exists between the level of conservatism and imported water purchase. It was found that the robust optimization approach addresses parameter uncertainty without excessively affecting the system. While we applied our methodology to hypothetical conditions, extensions to real-world systems with similar structure are straightforward. Therefore, our study shows that this approach is a useful tool in water supply system design that prevents system failure at a certain level of risk.     

A supply chain contract with flexibility as a risk-sharing mechanism for demand forecasting

Demand forecasting is one of the main causes of the bullwhip effect in a supply chain. As a countermeasure for demand uncertainty as well as a risk-sharing mechanism for demand forecasting in a supply chain, this article studies a bilateral contract with order quantity flexibility. Under the contract, the buyer places orders in advance for the predetermined horizons and makes minimum purchase commitments. The supplier, in return, provides the buyer with the flexibility to adjust the order quantities later, according to the most updated demand information. To conduct comparative simulations, four-echelon supply chain models, that employ the contracts and different forecasting techniques under dynamic market demands, are developed. The simulation outcomes show that demand fluctuation can be effectively absorbed by the contract scheme, which enables better inventory management and customer service. Furthermore, it has been verified that the contract scheme under study plays a role as an effective coordination mechanism in a decentralised supply chain.     

System Dynamics Forecasting on Taiwan Power Supply Chain

This research aims to study the sustainability of Taiwan power supply chain based on system dynamics forecasting. The paper tries to investigate electricity shortage effects not only on the industrial side, but also from the standpoint of society. In our model, different forecasting methods such as linear regression, time series analysis, and gray forecasting are also considered to predict the parameters. Further tests such as the structure, dimension, historical fit, and sensitivity of the model are also conducted in this paper. Through analysis forecasting result, we believe that the demand for electricity in Taiwan will continue to increase to a certain level for a period of time in the future. This phenomenon is closely related to Taiwan’s economic development, especially industrial development. We also point out that electricity prices in Taiwan do not match with high industrial demand, and that prices are still slightly low. Finally, the future growth trend of Taiwan’s electricity demand has not changed, and ensuring adequate supply to meet electricity demand to prevent potential power shortages will pose some difficulty.     

Application of neural networks in forecasting engine systems reliability

This paper presents a comparative study of the predictive performances of neural network time series models for forecasting failures and reliability in engine systems. Traditionally, failure data analysis requires specifications of parametric failure distributions and justifications of certain assumptions, which are at times difficult to validate. On the other hand, the time series modeling technique using neural networks provides a promising alternative. Neural network modeling via feed-forward multilayer perceptron (MLP) suffers from local minima problems and long computation time. The radial basis function (RBF) neural network architecture is found to be a viable alternative due to its shorter training time. Illustrative examples using reliability testing and field data showed that the proposed model results in comparable or better predictive performance than traditional MLP model and the linear benchmark based on Box–Jenkins autoregressive-integrated-moving average (ARIMA) models. The effects of input window size and hidden layer nodes are further investigated. Appropriate design topologies can be determined via sensitivity analysis.     

A micro-model of internal supply forecasting for technical manpower planning

The manpower planning process includes forecasting the future demand for manpower and the future internal supply of manpower and then developing action plans which will balance supply and demand. Many of the models which exist for forecasting internal supply are for periods of one year or longer, which makes them inappropriate for many project planning and short term human resource management applications. This paper presents an easily implemented short-range (12-month horizon) model for forecasting internal supply. Time series analysis techniques are used to identify seasonal patterns and trends which exist in the determinants of internal supply. These are employed in the development of an internal supply forecast at both the aggregate firm level and at the individual skill group level. Feasibility of the model is demonstrated using empirical data. Output of the model is useful for further manpower planning.     

Daily Urban Water Demand Forecasting Based on Chaotic Theory and Continuous Deep Belief Neural Network

The prediction of daily water demands is a crucial part of the effective functioning of the water supply system. This work proposed that a continuous deep belief neural network (CDBNN) model based on the chaotic theory should be implemented to predict the daily water demand time series in Zhuzhou, China. CDBNN should initially be used to predict the urban water demand time series. First, the power spectrum and the largest Lyapunov exponent is used to determine the chaotic characteristic of the daily water demand time series. Second, C–C method is utilized to reconstruct the water demand time series’ phase space. Lastly, the forecasting model should be produced with the continuous deep belief network and neural network algorithms implemented for feature learning and regression, respectively, and the CDBNN input established by the best embedding dimension of the reconstructed phase space. The proposed method is contrasted with the support vector regression, generalized regression neural networks and feed forward neural networks, and they are accepted with the identical dataset. The predictive performance of the models is examined using normalized root-mean-square error (NRMSE), correlation coefficient (COR), and mean absolute percentage error (MAPE). The results suggest that the hybrid model has the smallest NRMSE and MAPE values, and the largest COR.     

Surveying stock market forecasting techniques – Part II: Soft computing methods

The key to successful stock market forecasting is achieving best results with minimum required input data. Given stock market model uncertainty, soft computing techniques are viable candidates to capture stock market nonlinear relations returning significant forecasting results with not necessarily prior knowledge of input data statistical distributions. This paper surveys more than 100 related published articles that focus on neural and neuro-fuzzy techniques derived and applied to forecast stock markets. Classifications are made in terms of input data, forecasting methodology, performance evaluation and performance measures used. Through the surveyed papers, it is shown that soft computing techniques are widely accepted to studying and evaluating stock market behavior.     

ANN-based residential water end-use demand forecasting model

Bottom-up urban water demand forecasting based on empirical data for individual water end uses or micro-components (e.g., toilet, shower, etc.) for different households of varying characteristics is undoubtedly superior to top-down estimates originating from bulk water metres that are currently performed. Residential water end-use studies partially enabled by modern smart metering technologies such as those used in the South East Queensland Residential End Use Study (SEQREUS) provide the opportunity to align disaggregated water end-use demand for households with an extensive database covering household demographic, socio-economic and water appliance stock efficiency information. Artificial neural networks (ANNs) provide the ideal technique for aligning these databases to extract the key determinants for each water end-use category, with the view to building a residential water end-use demand forecasting model. Three conventional ANNs were used: two feed-forward back propagation networks and one radial basis function network. A sigmoid activation hidden layer and linear activation output layer produced the most accurate forecasting models. The end-use forecasting models had R² values of 0.33, 0.37, 0.60, 0.57, 0.57, 0.21 and 0.41 for toilet, tap, shower, clothes washer, dishwasher, bath and total internal demand, respectively. All of the forecasting models except the bath demand were able to reproduce the means and medians of the frequency distributions of the training and validation sets. This study concludes with an application of the developed forecasting model for predicting the water savings derived from a citywide implementation of a residential water appliance retrofit program (i.e., retrofitting with efficient toilets, clothes washers and shower heads).     

Impact of the shape of demand distribution in decision models for operations management

Decision support tools are increasingly used in operations where key decision inputs such as demand, quality, or costs are uncertain. Often such uncertainties are modeled with probability distributions, but very little attention is given to the shape of the distributions. For example, state-of-the-art planning systems have weak, if any, capabilities to account for the distribution shape. We consider demand uncertainties of different shapes and show that the shape can considerably change the optimal decision recommendations of decision models. Inspired by discussions with a leading consumer electronics manufacturer, we analyze how four plausible demand distributions affect three representative decision models that can be employed in support of inventory management, supply contract selection and capacity planning decisions. It is found, for example, that in supply contracts flexibility is much more appreciated if demand is negatively skewed, i.e., has downside potential, compared to positively skewed demand. We then analyze the value of distributional information in the light of these models to find out how the scope of improvement actions that aim to decrease demand uncertainty vary depending on the decision to be made. Based on the results, we present guidelines for effective utilization of probability distributions in decision models for operations management.     

Incorporating weather uncertainty in demand forecasts for electricity market planning

A major component of electricity network planning is to ensure supply capability into the future, through generation and transmission development. Accurate forecasts of maximum demand are a crucial component of this process, with future weather conditions having a large impact on forecast accuracy. This article presents an improved methodology for the consideration of weather uncertainty in electricity demand forecasts. Case studies based on the Australian national electricity market are used to validate the proposed methodology.     

Water allocation improvement in river basin using Adaptive Neural Fuzzy Reinforcement Learning approach

An accurate simulation model is a necessary tool for optimizing allocation of scarce water resources in large-scale river basins. Adaptive Neural Fuzzy Inference System (ANFIS) method is used to simulate seven interconnected sub-basins in a regional river system located in Iran. Simulated predictions of the method are compared with historical data measurements. ANFIS is a powerful tool for simulating water resources systems of all sub-basins. In this study, a new methodology, Adaptive Neural Fuzzy Reinforcement Learning (ANFRL) is presented for obtaining optimal values of the decision variables. By combining ANFIS with Fuzzy Reinforcement Learning within the content of historical data over a consecutive monthly management period, ANFRL method was derived. Based upon the results of this research, this methodology can be used to develop fuzzy rule systems that accurately simulate the behavior of complex river basin systems within the context of uncertainty. As previous researches have shown that, when simulation model accurately reproduces observed river basin behavior, the optimization model yields better results. Application of this approach in the present case study shows that the effects of uncertainty, imprecise and random factors are 21, 11 and 15% over water resources system, water demand estimated and hydrological regime, respectively. Finally, the results of this method showed that about 16% improvement in water allocation was attained when compared to the primary water resources management in this case study.     

Validation and reconstruction of flow meter data in the Barcelona water distribution network

This paper presents a signal analysis methodology to validate (detect) and reconstruct the missing and false data of a large set of flow meters in the telecontrol system of a water distribution network. The proposed methodology is based on two time-scale forecasting models: a daily model based on a ARIMA time series, while the 10-min model is based on distributing the daily flow using a 10-min demand pattern. The demand patterns have been determined using two methods: correlation analysis and an unsupervised fuzzy logic classification, named LAMDA algorithm. Finally, the proposed methodology has been applied to the Barcelona water distribution network, providing very good results.     

Improved supply chain management based on hybrid demand forecasts

Demand forecasts play a crucial role for supply chain management. The future demand for a certain product is the basis for the respective replenishment systems. Several forecasting techniques have been developed, each one with its particular advantages and disadvantages compared to other approaches. This motivates the development of hybrid systems combining different techniques and their respective strengths. In this paper, we present a hybrid intelligent system combining Autoregressive Integrated Moving Average (ARIMA) models and neural networks for demand forecasting. We show improvements in forecasting accuracy and propose a replenishment system for a Chilean supermarket, which leads simultaneously to fewer sales failures and lower inventory levels than the previous solution.     

Controller design and reduction of bullwhip for a model supply chain system using z-transform analysis

In this work, a discrete time series model of a supply chain system is derived using material balances and information flow. Transfer functions for each unit in the supply chain are obtained by z-transform. The entire chain can be modeled by combining these transfer functions into a close loop transfer function for the network. The model proves to be very useful in revealing the dynamics characteristic of the system. The system can be viewed as a linear discrete system with lead time and operating constraints. The stability of the system can be analyzed using the characteristic equation. Controllers are designed using frequency analysis. The bullwhip effect, i.e. magnification of amplitudes of demand perturbations from the tail to upstream levels of the supply chain, is a very important phenomenon for supply chain systems. We proved that intuitive operation of a supply chain system with demand forecasting will cause bullwhip. Moreover, lead time alone would not cause bullwhip. It does so only when accompanied by demand forecasting. Furthermore, we show that by implementing a proportional intergral or a cascade inventory position control and properly synthesizing the controller parameters, we can effectively suppress the bullwhip effect. Moreover, the cascade control structure is superior in meeting customer demand due to its better tracking of long term trends of customer demand.     

Effects of uncertainty on a tire closed-loop supply chain network

In a closed-loop supply chain (CLSC) network, there are both forward and reverse supply chains. In this research, a tire remanufacturing CLSC network is designed and optimized based on tire recovery options. The objective of the optimization model is to maximize the total profit. The optimization model includes multiple products, suppliers, plants, retailers, demand markets, and drop-off depots. The application of the model is discussed based on a realistic network in Toronto, Canada using map. In addition, a new decision tree-based methodology is provided to calculate the net present value of the problem in multiple periods under different sources of uncertainty such as demand and returns. Furthermore, the discount cash flow is considered in the methodology as a novel innovative approach. This methodology can be applied in comparing the profitability of different design options for CLSCs.     

计及多元不确定性的综合能源系统优化配置

综合能源系统的优化配置关键在于设备选型与数量配置,而能源负荷负荷和可再生能源出力预测误差以及故障发生的不确定性将直接影响配置方案的合理性以及经济性.为此,本文提出一种考虑源–网–荷多元不确定性的综合能源系统多目标–机会约束规划方法.考虑可再生能源出力与负荷需求预测误差引起的不确定性,本文构建了满足置信概率的能量供需平衡约束;针对供能网络中设备N-1故障引起的不确定性,提出调整裕度模型,进而构建了调整裕度与N-1设备能量缺额的机会约束.对于获得的帕累托解集,采用信息熵与逼近理想排序法构建多准则评价模型,以确定最优的系统配置.将本文方法应用于某区域综合能源系统的最优结构设计,实验结果表明,本文方法的有效性与可靠性.     

博弈改进的GM（1，1）在供应链需求预测中的应用

为解决供应链中需求预测精度不高的问题,在传统GM（1,1）预测模型的基础上,采用了一种基于遗传算法调整发展系数和内生灰作用量的灰色预测模型,运用此模型对供应链中各级需求量进行预测,使用博弈理论构建了供应链中各级需求预测出现误差时的协商策略用以对预测结果的优化。实验结果表明,协商策略获取了符合双方利益的需求量,预测结果有了较高的精度。