Demand Meter Research Articles

Modeling of Water Distribution System Based on Ten-Minute Accuracy Remote Smart Demand Meters

In the process of water distribution system modeling, nodal demands have been deemed to be the most significant input parameters causing uncertainties. Conventionally, users of the same type are assigned the same demand pattern, which does not reflect the variability in demand patterns. In Hefei City, a lot of remote smart demand meters were installed in recent years, providing real-time water consumption data for different water user sites. In this study, the water consumption data based on 10 min accuracy were collected and used in the established EPANET model of the study area. A 48 h simulation, including workdays and weekends, was carried out, and each node’s base demand and demand pattern were accurately calculated according to the remote data. The results show that the accuracy of the model has met the modeling criteria (less than 2 m for all pressure monitoring points), and there is no need to calibrate the nodal demand. The established offline hydraulic model can better reflect the water consumption characteristics of each type of user and has revealed the significant influence of secondary water supply systems.

Comparison of Imputation Methods for End-User Demands in Water Distribution Systems

AbstractThis study examines the impact of advanced metering infrastructure (AMI) end-user demand metering failure on water distribution system (WDS) operation and management. To address this issue,...

Short-Term Forecasting of Household Water Demand in the UK Using an Interpretable Machine Learning Approach

This study utilizes a rich UK data set of smart demand metering data, household characteristics, and weather data to develop a demand forecasting methodology that combines the high accuracy of machine learning models with the interpretability of statistical methods. For this reason, a random forest model is used to predict daily demands 1 day ahead for groups of properties (mean of 3.8 households/group) with homogenous characteristics. A variety of interpretable machine learning techniques [variable permutation, accumulated local effects (ALE) plots, and individual conditional expectation (ICE) curves] are used to quantify the influence of these predictors (temporal, weather, and household characteristics) on water consumption. Results show that when past consumption data are available, they are the most important explanatory factor. However, when they are not, a combination of household and temporal characteristics can be used to produce a credible model with similar forecasting accuracy. Weather input has overall a mild to no effect on the model's output, although this effect can become significant under certain conditions.

Efficient Leak Localization in Water Distribution Systems Using Multistage Optimal Valve Operations and Smart Demand Metering

AbstractThis paper proposes a multistage method for burst leak localization through valve operations (VOs) and smart demand metering in district meter areas (DMAs) of water distribution systems (WDSs). Each stage includes partitioning of the DMA into two subregions using VOs and identification of potentially leaking pipes within the subregions through water balance analysis based on smart demand meters. Such a process is performed repeatedly (multiple stages) to narrow down the spatial range for pinpointing leak locations. To improve efficiency, a bisection optimization problem is formulated to localize the minimum leak areas using the lowest number of VOs, which is solved by a graph theory‐based method. The utility of the proposed method is demonstrated using two DMAs (DMA1 and DMA2) of a real WDS with different topological properties. Results show that the proposed method can efficiently localize artificial burst leaks in DMA1 within 7–15% of the total pipe length, implying that the proposed method is theoretically effective in localizing pipe burst leaks. The real application to DMA2 has identified two leak regions with 2.3 and 4.2 km of pipe length (around 3–6% of the entire DMA2) using 18 VOs. These two burst leaks have been subsequently confirmed and pinpointed using listening rods by practitioners of the local water utility. These results indicate that the proposed multistage method is effective and efficient for burst leak localization, which can be promising for wide practical applications due to rapid developments of smart WDSs (e.g., smart demand meters or control valves).

Prototype Development Open Source Platform-Based Electricity Meter with Minimum Rate of Change According to Module Five of the Electricity Distribution Procedure in the Brazilian National Electrical System (PRODIST)

The article proposes the development of a low-cost, well-performing open source residential energy consumption demand meter compared to commercial meters in order to achieve accurate results and remain within the limits determined by technical standards, with the objective of measuring real and instant energy consumption through microcontroller and current sensor. The prototype has the ability to provide energy consumption data for a low voltage residential load or wiring to obtain the final amount of energy consumption Data is collected in stages by collecting current and voltage, the microcontroller receives analog stimuli from sensors, as voltage and current samples are processed to calculate power, current and voltage. The use of the device leads the consumer to clarify the instantaneous energy consumption per installed component and it is possible to compare the cost benefit in relation to the energy consumption, providing consumers the follow up and a detailed view of its consumption, leading to efficient and sustainable use, enabling the improvement of the quality of energy supply to equipment connected to the distribution system.

Design and fabrication of nanocomposite-based polyurethane filter for‏ ‏improving municipal waste water quality and removing organic pollutants

Nanotechnology has been used in different industries for years. In this study, polyurethane filter, modified with nano-sized polypyrrole–ZnO was used for wastewater quality improvement. The effect of coating method and influential parameters on polymer morphology was studied by Fourier transform infrared spectroscopy and scanning electron microscopy. The results revealed that the uniformly synthesized polymers are seed like. The size of synthesized nanoparticles was observed to be about 50–120 nm. The effect of the number of iterative filtration and the height of the filter on improving the quality of the waste water was investigated using central composite design. After filtration, spectroscopy method, gas chromatography method, and some other devices such as biochemical oxygen demand meter and salt meter were used to evaluate the quality of the waste water. The results indicated that the filter efficiency in optimizing parameters such as total dissolved solids, biochemical oxygen demand, chemical oxygen demand, color, salinity, hardness, pH, and organic compounds removal is desirable. After data modeling, the optimal thickness of the filter was 3.8 cm and the most appropriate iteration for filtration was eight times obtained using a graphical method. Results showed that the designed filter had an excellent ability to improve wastewater quality and can be used in water and wastewater refining instruments.

Implications of data sampling resolution on water use simulation, end-use disaggregation, and demand management

Understanding the tradeoff between the information of high-resolution water use data and the costs of smart meters to collect data with sub-minute resolution is crucial to inform smart meter networks. To explore this tradeoff, we first present STREaM, a STochastic Residential water End-use Model that generates synthetic water end-use time series with 10-s and progressively coarser sampling resolutions. Second, we apply a comparative framework to STREaM output and assess the impact of data sampling resolution on end-use disaggregation, post meter leak detection, peak demand estimation, data storage, and meter availability. Our findings show that increased sampling resolution allows more accurate end-use disaggregation, prompt water leakage detection, and accurate and timely estimates of peak demand. Simultaneously, data storage requirements and limited product availability mean most large-scale, commercial smart metering deployments sense data with hourly, daily, or coarser sampling frequencies. Overall, this work provides insights for further research and commercial deployment of smart water meters.

Enhancing water system models by integrating big data

The past quarter century has witnessed development of advanced modeling approaches, such as stochastic and agent-based modeling, to sustainably manage water systems in the presence of deep uncertainty and complexity. However, all too often data inputs for these powerful models are sparse and outdated, yielding unreliable results. Advancements in sensor and communication technologies have allowed for the ubiquitous deployment of sensors in water resources systems and beyond, providing high-frequency data. Processing the large amount of heterogeneous data collected is non-trivial and exceeds the capacity of traditional data warehousing and processing approaches. In the past decade, significant advances have been made in the storage, distribution, querying, and analysis of big data. Many tools have been developed by computer and data scientists to facilitate the manipulation of large datasets and create pipelines to transmit the data from data warehouses to computational analytic tools. A generic framework is presented to complete the data cycle for a water system. The data cycle presents an approach for integrating high-frequency data into existing water-related models and analyses, while highlighting some of the more helpful data management tools. The data tools are helpful to make sustainable decisions, which satisfy the objectives of a society. Data analytics distribution tool Spark is introduced through the illustrative application of coupling high-frequency demand metering data with a water distribution model. By updating the model in near real-time, the analysis is more accurate and can expose serious misinterpretations.

The performance of demand meters under varying load conditions

The influence of load variations on the performance of four different types of electricity demand meters was studied using a computer controlled test system capable of reproducing load profile data recorded in the field. Results of the tested meters are compared with computer calculated demand values based on readings of a fast response reference multimeter. Differences in readings in the range of -9.3% to +5.3% were observed during the tests of the four demand meters. For the load profiles considered, demand calculations based on one minute subintervals resulted in values closer to the true peak demand than calculations with five minute subintervals. >

Software design and evaluation of a microcomputer-based automated load forecasting system

Several electric utilities (viz., Virginia Power) in the United States apply demand charge on the basis of coincidental peak hour load (PHL). This is in addition to the usual kWhr charge. The hour during which the utility experiences the highest demand (MW) for the month is called the peak hour and this MW value is referred to here as the PHL. The hourly demand meter readings for the appropriate consumer (e.g., industrial or large commercial customer, wholesale purchaser, etc.) are examined to determine their demand during the peak hour of the month. The monthly demand charge is computed on the basis of the consumer's contribution to this PHL. This monthly capacity charge can run into millions of dollars for industrial and large commercial customers. Thus it is very valuable for the concerned consumer to receive advance information about the time and size of the utility's peak load. This information would enable them to take load control actions such that their demand can be reduced during the hour of peak load. The motivation behind the research reported in this paper is precisely this-to be able to predict the time and size of this PHL. The load forecast software is part of a load forecasting-load management decision support system based on microcomputers. An IBM-RT/PC is used as the central computer around which the load forecasting and load management simulator has been built in our laboratory.

Software Design and Evaluation of a Microcomputer-Based Automated Load Forecasting System

Several electric utilities (viz., Virginia Power) in the United States apply demand charge on the basis of coincidental peak hour load (PHL). This is in addition to the usual kWhr charge. The hour during which the utility experiences the highest demand (MW) for the month is called the peak hour and this MW value is referred to here as the PHL. The hourly demand meter readings for the appropriate consumer (e.g., industrial or large commercial customer, wholesale purchaser, etc.) are examined to determine their demand during the peak hour of the month. The monthly demand charge is computed on the basis of the consumer's contribution to this PHL. This monthly capacity charge can run into millions of dollars for industrial and large commercial customers. Thus it is very valuable for the concerned consumer to receive advance information about the time and size of the utility's peak load. This information would enable them to take load control actions such that their demand can be reduced during the hour of peak load. The motivation behind the research reported in this paper is precisely this-to be able to predict the time and size of this PHL. The load forecast software is part of a load forecasting-load management decision support system based on microcomputers. An IBM-RT/PC is used as the central computer around which the load forecasting and load management simulator has been built in our laboratory.

Application of a three phase nonsinusoidal calibration system for testing energy and demand meters under simulated field conditions

The influence of harmonics on commonly used three-phase watthour and kVA demand meters was studied using a computer-controlled three-phase test system capable of generating nonsinusoidal voltages and currents. A description of the three-phase test system and results of preliminary tests of several revenue meters are presented. The performance of the tested meters is compared with the response of time-division and thermal reference meters for different harmonic conditions, primarily field-recorded data. Errors up to 2% were observed during the testing of energy meters and up to 35% for kVA demand meters.< <ETX xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">&gt;</ETX>

Rural Electric Demand Research

In cooperative research with rural power suppliers in Nebraska, 35 recording demand meters were installed. The demand study was started in December of 1980 and concluded in March of 1983. Data collection in Nebraska was concentrated in a five-county area in eastcentral Nebraska. Customers were sampled based on a stratified randomized design. Electrical demands were recorded for 5-min intervals. Coincidental peak demands for 60-min intervals were determined for selected peak power use days in 1981 and 1982. In addition, 15-min coincidental demands showing maximum average and minimum demands for each month for the two-year study are presented. Demands predicted by the Rural Electrification Administration demand tables were generally lower than the measured demands while demands predicted by linear regression analysis were higher than the measured demands. Measured coincidental peak demands per customer for the total of all stratified groups ranged from 5.7 to 8.6 kW.

Old Water‐New Water: Setting Equitable Water Rates for Suburban Customers

The rate concepts applied in the Seattle, Wash., metropolitan area address the questions of how water system costs should be split between city and suburban customers, who should pay for suburban growth, and what methods should be used to compute revenues required from wholesale purveyors. Seattle Water Department contracts with suburban wholesale purveyors include allocation of the costs of both old and new facilities, demand metering, and separate rates for supplying “old water” and the additional “new water” needed to meet the demand created by suburban growth.

Load Research and Its Application to Electricity Demand Forecasting

This paper describes the actual systems for half-hourly demand metering of consumers, together with the scope of the research programme in covering different types of consumer and electricity usage, and the statistical methodology involved. Demand patterns obtained from the research may be linked with sales (TWh), actual or estimated, to build total system load curves. This approach, which automatically compensates for differing mixes of consumers or sales, is demonstrated by preparing a mediumterm (seven year) of system maximum demand. As an aid to evaluation the forecast method is applied to historic data and is seen to provide an excellent explanation of changes in the relationship between sales and demand (expressed in terms of unrestricted load factor) which have occurred over the last 15 years. The method may be applied from any base year and is readily able to absorb new or improved data whilst also providing an indication of where future research could most usefully be directed.

New technology for electrical metering monitoring and control instrumentation

Presently, individual instruments are available at reasonable costs for metering, monitoring and controlling the use of electrical energy. Many of these instruments have been developed in the last few years and make varying degrees of use of sophisticated electronic components and microprocessor chips. Performance data collected on the use of these instruments have shown that there are substantial energy and cost savings possible from widespread application of these devices. The purpose of this paper is to enumerate briefly some data on the costs and benefits of instruments which allow time of day and demand metering, monitoring of energy use and cost, direct load control and indirect load control. Finally, an integrated instrument is proposed which combines these varied functions into one low cost device to provides cost effective electrical energy conservation.

Expected welfare gains from peak-load electricity charges

To estimate the benefits of peak-load electricity pricing it is necessary to consider the demand curves of different types of consumer within each subperiod. In this paper the author first examines a two-period case with two types of consumer and one type of generating technology. This illustrates the complexity of analysing the welfare gains associated with demand meters. Most of the simplifying assumptions are then relaxed and consideration is given to some of the complications which arise in a multi-period world with more than one type of technology for producing electric power. Finally the author considers questions of equity as well as efficiency in the pricing of electricity in the USA.

Industry View of Demand Metering

Open communications between industries and utilities, based on awareness of each others' needs and problems, will give proper analysis of demand metering's impact on individual plants.

Water Rates for Industry Should be Equitable

In many instances across the country water rates for industry are extremely low, possibly loss rates; and in other areas, where industry is captive, industrial rates are extremely high while domestic rates are far too low. It is becoming increasingly accepted practice to consider the matter of system demand, both hourly and daily peaks on the cost of water service. Demand metering may be the most practical solution.

Optimum furnace operation and demand control

In applying demand control to a furnace shop, the preferable procedure from a furnace operational point of view would be to have the control not interfere with the furnace operation. However, in the course of carrying out its functions it has to call for changes in furnace power level, changes which may not be in keeping with optimum furnace operation. The reduction in electric power billing must be weighed against the corresponding degree of interference with plant operation and the possibility of reduced production. Higher cost, more sophisticated controls can be expected to control within a smaller “acceptable” zone and to maintain control closer to the preset demand level without exceeding it. As the controls become more costly and more sophisticated this ability to improve on the performance of the next lower cost unit becomes more limited. The worth of the control should be based, therefore, on the return on investment versus cost for the difference between that control and the next lower cost unit. A large, high power furnace shop will find economic advantage in applying digital computer controls. The small furnace shop will find that a demand meter with auxiliary controls is economically justifiable; it may also find that more costly controls cannot be justified due to their ability to provide only limited additional return on investment.