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).     

A microsimulation model of urban energy use: Modelling residential space heating demand in ILUTE

Rapid urbanization, climate change and energy security warrant a more detailed understanding of how cities today consume energy. Agent-based, integrated microsimulation models of urban systems provide an excellent platform to accomplish this task, as they can capture both the short- and long-term decisions of firms and households which directly affect urban energy consumption. This paper presents the current effort towards developing an urban energy model for the Integrated Land Use, Transportation, Environment (ILUTE) modelling system.     

An Agent-based behavioral simulation model for residential water demand management: The case-study of Tehran,Iran

One of the main parts of urban water consumption is residential water use. In this paper, a new framework based on agent-based modeling is introduced to simulate the behavioral characteristics of residential water users and their social interactions. The proposed framework, as a decision making tool, can be used for evaluating responses of domestic water users to some factors such as social consciousness about hydrologic conditions, water pricing, and advertising policies as well as social interactions and communications. To illustrate the practical utility of the framework, it is applied to the western part of Tehran metropolitan city in Iran. The results show that increasing water price and investing on advertisement can be considered as effective strategies for managing residential water consumption in the study area.     

Parameterizing residential water demand pulse models through smart meter readings

This paper proposes a method for parameterizing the Poisson models for residential water demand pulse generation, which consider the dependence of pulse duration and intensity. The method can be applied to consumption data collected in households through smart metering technologies. It is based on numerically searching for the model parameter values associated with pulse frequencies, durations and intensities, which lead to preservation of the mean demand volume and of the cumulative trend of demand volumes, at various time aggregation scales at the same time. The method is applied to various case studies, by using two time aggregation scales for demand volumes, i.e. fine aggregation scale (1 min or 15 min) and coarse aggregation scale (1 day). The fine scale coincides with the time resolution for reading acquisition through smart metering whereas the coarse scale is obtained by aggregating the consumption values recorded at the fine scale.Results show that the parameterization method presented makes the Poisson model effective at reproducing the measured demand volumes aggregated at both time scales. Consistency of the pulses improves as the fine scale resolution increases.     

蚁群算法在需水预测模型参数优化中的应用

为了解决投影寻踪（PP）需水预测模型的高维、非正态、非线性参数优化问题,提高需水预测的精度,尝试用基于网格划分的自适应连续域蚁群算法(ACA)在不同拟合和预测时长内对模型参数进行优化组合,并运用该模型进行年需水量预测。基于改进蚁群算法的投影寻踪需水预测模型参数优化进行了实例仿真。对基于改进蚁群算法的预测精度与基于人工免疫算法（AIA）和BP神经网络的模型(BPANN)参数优化结果分别进行了比较,实验结果表明：1）这三种算法的拟合精度相对误差绝对值分别小于2%、10%和10%;2）预测精度相对误差绝对值分别小于6%、11%和12%;3）改进蚁群算法能收敛到全局最优解,收敛速度较快。因此,改进蚁群算法的投影寻踪需水预测结果明显优于人工免疫算法和BP神经网络。该方法可推广到其他类似的高维非线性问题上。     

Agent-based joint model of residential location choice and real estate price for land use and transport model

Residential location choice (RLC) and real estate price (REP) models are traditional and key components of land use and transport model. In this study, an agent-based joint model of RLC and REP (RLC–REP model) was proposed for SelfSim, an agent-based dynamic evolution of land use and transport model. The RLC–REP model is capable of simulating the negotiation between the active household agents (buyers) and owner agents (sellers) using agent-based modeling. In particular, both utility maximization theory and prospect theory were used to develop a utility function to simulate the location choice behavior of active household agents. The utility function incorporates only two variables: house price and accessibility. The latter variable is calculated using MATSim, an activity-based model. The asking price behavior of owner agents is based on three specific rules. The residential location choices of household agents and house prices can be obtained by negotiation. Finally, genetic algorithm was used to estimate the parameters of the RLC–REP model. The calibrated model was tested in Baoding, a medium-sized city in China, and historical validation was performed to assess its performance. The results suggest that the forecasting ability of the RLC–REP model in terms of real estate price is satisfactory.     

Implementation of a building energy management system for residential demand response

Demand response is proposed as a solution to handle the fluctuations in the power supply in a scenario with higher penetration of renewable energy sources. Although demand response already offers a positive business case in certain domains, it still lacks maturity in other areas, especially in the residential domain. This paper presents a comprehensive study of a novel BEMS to strengthen the adoption of residential demand response. The proposed consumer-centric BEMS monitors the building performance and its surroundings, interacts with the residents, optimally controls DERs and provides demand response to an aggregator. The BEMS is conceived with a multimodal objective: exploit flexible consumption through demand response and run the building in a energy efficient manner. The system architecture and hardware and software design are detailed. A prototype of the envisioned BEMS has been developed and deployed in a 12-storey residential building. The prototype performance, the scalability, the data monitoring capabilities, and the interaction with the residents and controllability of DERs of the BEMS are demonstrated. Moreover, the study provides an estimated of the total flexibility potential of the testbed.     

An intelligent pattern recognition model to automate the categorisation of residential water end-use events

The rapid dissemination of residential water end-use (e.g. shower, clothes washer, etc.) consumption data to the customer via a web-enabled portal interface is becoming feasible through the advent of high resolution smart metering technologies. However, in order to achieve this paradigm shift in residential customer water use feedback, an automated approach for disaggregating complex water flow trace signatures into a registry of end-use event categories needs to be developed. This outcome is achieved by applying a hybrid combination of gradient vector filtering, Hidden Markov Model (HMM) and Dynamic Time Warping Algorithm (DTW) techniques on an existing residential water end-use database of 252 households located in South-east Queensland, Australia having high resolution water meters (0.0139 L/pulse), remote data transfer loggers (5 s logging) and completed household water appliance audits. The approach enables both single independent events (e.g. shower event) and combined events (i.e. several overlapping single events) to be disaggregated from flow data into a comprehensive end-use event registry. Complex blind source separation of concurrently occurring water end use events (e.g. shower and toilet flush occurring in same time period) is the primary focus of this present study. Validation of the developed model is achieved through an examination of 50 independent combined events.     

Disaggregating the components of a monthly water resources system model to daily values for use with a water quality model

A model designed to disaggregate the water balance components of a monthly water resources system model to daily time series is presented. The objective of the model is to add value to existing monthly model setups and to provide daily water balance data for a water quality model. The model components include the disaggregation of incremental catchment flows, run-of-river and reservoir abstractions, reservoir releases for users and environmental flow requirements and reservoir spills. While previous studies have demonstrated that the main incremental catchment flow component is fit-for-purpose, the overall model is difficult to validate due to the impacts of imperfect monthly model simulations and the variability in operational practises compared with operational design procedures that form the basis of the model algorithm. Despite these reservations, the model is considered to provide a pragmatic, but useful approach to disaggregating monthly water balance simulations for use within a daily water quality model.     

A single-period inventory model with fuzzy demand

The single-period inventory models have wide applications in the real world in assisting the decision maker to determine the optimal quantity to order. Due to lack of historical data, the demand has to be subjectively determined in many cases. In this paper, a single-period inventory model for cases of fuzzy demand is constructed. The costs considered include the procurement cost, shortage cost, and holding cost. For different fuzzy total cost resulted from different order quantity, a method for ranking fuzzy numbers is adopted to find the optimal order quantity in terms of the cost. When the profit gained from selling one item is less (greater) than the loss incurred due to one unsold item, the optimal order quantity lies in the range defined for the left-shape (right-shape) function of the fuzzy demand. If the unit profit is equal to the unit loss, then all quantities with a membership grade 1 are optimal to be ordered. The methodology of this paper can be applied to construct other inventory models with fuzzy demand.     

A multi-warehouse inventory model for items with time-varying demand and shortages

This paper develops a deterministic replenishment model with multiple warehouses (one is an owned warehouse and others are rented warehouses) possessing limited storage capacity. In this model, the replenishment rate is infinite. The demand rate is a function of time and increases at a decreasing rate. The stocks of rented warehouses are transported to owned warehouse in continuous release pattern. The model allows shortages in owned warehouse and permits part of the backlogged shortages to turn into lost sales—which is assumed to be a function of the currently backlogged amount. The solution procedure for finding the optimal replenishment policy is shown. As a special case of the model, the corresponding models with completely backlogged shortages and without shortages are also presented. The models are illustrated with the help of numerical examples. Sensitivity analysis of parameters is given in graphical form.Scope and purposeIn practical inventory management, there exist many factors like an attracted price discount for bulk purchase, etc. to make retailers buy goods more than the capacity of their owned warehouse. In this case, retailers will need to rent other warehouses or to rebuild a new warehouse. However, from economical point of views, they usually choose to rent other warehouses. If there are multiple warehouses available, an important problem faced by the retailers is which warehouses to be selected to hold items replenished, when to replenish as well as what size to replenish. For such a problem, the existing two-warehouse models, based on an unrealistic assumption that the rented warehouse has unlimited storage capacity, presented some procedures for determining the optimal replenishment policy. This paper extends the existing two-warehouse models in three directions. Firstly, the traditional two-warehouse models assumed the storage capacity of the rented warehouse unlimited. The present paper relaxes this impractical assumption and considers the situation with multiple rented warehouses having a limited capacity. Secondly, the traditional two-warehouse models considered a constant demand rate or a linearly increasing demand rate. In this model, the demand rate varies over time and increases at a decreasing rate, which implies an increasing market going to saturation. Thirdly, we extend the two-warehouse models to the case with partially backlogged shortages. The purpose of this paper is to build a multi-warehouse replenishment model to help decision-makers solve the problem of which warehouses to be chosen to store items replenished and how to replenish.     

A passenger demand model for airline flight scheduling and fleet routing

Fleet routing and flight scheduling are essential to a carrier's profitability, its level of service and its competitive capability in the market. This research develops a model and a solution algorithm to help carriers simultaneously solve for better fleet routes and appropriate timetables. The model is formulated as an integer multiple commodity network flow problem. An algorithm based on Lagrangian relaxation, a sub-gradient method, the network simplex method, the least cost flow augmenting algorithm and the flow decomposition algorithm is developed to efficiently solve the problem. The results of a case study, regarding a major Taiwan airline's operations, show the model's good performance.Scope and purposeFleet routing and flight scheduling issues have been widely studied to enhance airline's operation efficiency. Normally, network flow techniques are adopted for modeling and solving such complex mathematical problems. However, traditional approaches, which employ draft timetable as an essential medium, not only involve too much subjective judgement and decision making in the process but also reveal an incapability of directly and systematically managing the interrelation between supply and demand. The purpose of this paper is to develop a network model together with a solution algorithm, that can directly manage the interrelationships between passenger trip demands and flight supplies, in order to effectively assist carriers’ scheduling.     

A capital investment model for elastic demand and non-linear production capacity

A methodology is developed that determines the most desirable capital investment under the conditions of an elastic demand that is time dependent and a non-linear production function. The methodology also identifies the optimum yearly production rates and unit selling prices. The methodology is implemented by a computer program.     

A portable miniature UV-LED-based photoelectrochemical system for determination of chemical oxygen demand in wastewater

A portable, cost-effective, environmental friendly and miniature thin-layer photoelectrochemical system in conjunction with an ultraviolet light emitting diode (UV-LED) is developed for determination of chemical oxygen demand (COD), namely UV-LED PeCOD. The COD value is directly quantified by measuring the amount of electrons captured at a nanostructured TiO₂ electrode during the exhaustive photoelectrocatalytic degradation of organic species in the thin-layer cell. The key parameters of the photoelectrochemical system, such as applied potential bias, light intensity and solution pH, were investigated and optimized. Combined with a microelectrochemical system and a laptop computer, the UV-LED PeCOD system enables end-users to perform on-site COD analysis in a simple, rapid, sensitive and accurate manner. Under the optimal conditions, the system can achieve a practical detection limit of 0.2 ppm COD with a linear range of 0–300 ppm COD. The proposed UV-LED PeCOD technology can potentially make a revolutionary improvement to the conventional COD analysis and may be widely used in water quality monitoring industry.     

A probabilistic methodology for quantifying,diagnosing and reducing model structural and predictive errors in short term water demand forecasting

Accurate forecasts of water demand are required for real-time control of water supply systems under normal and abnormal conditions. A methodology is presented for quantifying, diagnosing and reducing model structural and predictive errors for the development of short term water demand forecasting models. The methodology (re-)emphasises the importance of posterior predictive checks of modelling assumptions in model development, and to account for inherent demand uncertainty, quantifies model performance probabilistically through evaluation of the sharpness and reliability of model predictive distributions. The methodology, when applied to forecast demand for three District Meter Areas in the UK, revealed the inappropriateness of simplistic Gaussian residual assumptions in demand forecasting. An iteratively revised, parsimonious model using a formal Bayesian likelihood function that accounts for kurtosis and heteroscedasticity in the residuals led to sharper yet reliable predictive distributions that better quantifies the time varying nature of demand uncertainty across the day in water supply systems.     

A geospatially-enabled web tool for urban water demand forecasting and assessment of alternative urban water management strategies

This study develops and demonstrates the Integrated Urban Water Model (IUWM) for forecasting urban water demand with options to assess effects of water conservation and reuse. While water and energy balance drive hydrologic, storage and recycling simulations on a daily timestep, social and infrastructural processes are resolved by spatially distributed parameters. IUWM is deployed as an online tool with geographical information system (GIS) interfaces, enhancing its ease of use and applicability at building to municipal scales. The performance of the model at varying spatial scales was evaluated with extensive water metering data for the City of Fort Collins, Colorado. The calibrated model provided very good estimates of demands at individual block group as well as the municipal service area. The capacity of IUWM for the assessment of the spatiotemporal variability of water consumption and effects of water demand management strategies under climate and urban growth scenarios is discussed.     

An accessibility measure for the combined travel demand model

The accessibility-oriented transport planning method is critical for many high population density cities in China. Most definitions of accessibility only consider spatial separation and ignore the influence of traveler choice on accessibility. In this paper, the combined travel demand model is employed in transport planning. The travelers’ choice behavior of the model is based on the random utility theory. The model overcomes inconsistence problem of the sequential four-step model on travel behavior and con...     

A rule-based water quality model for PC-environment

A package for constructing rule-based models for water quality management in lakes was developed. The preliminary application deals with prediction of bluegreen algal blooms in a lake. An expert system cell VP-EXPERT was coupled with TURBO PASCAL 5.0 and dBASE-IV. The rule-base is used as a predictive probabilistic model, and the prediction is updated with the past distribution of observations from the calendar month predicted. The approach was found attractive and further studies are justified, both in developing the present application, and in testing the applicability of PROLOG in coding the system.