Статті в журналах: "Load schedule"

1

Catană, Mădălin Gabriel, Sergiu Tonoiu, and Mihail Purcărea. "Bidirectional Load Planning Procedure for Job-Shops with Availability Time-Windows for Specific Machines." Applied Mechanics and Materials 760 (May 2015): 181–86. http://dx.doi.org/10.4028/www.scientific.net/amm.760.181.

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This paper presents a bidirectional load planning procedure for job-shops with availability time-windows for specific machines. This heuristic procedure uses priority rules and reverse planning directions to progressively construct two schedules: one for machines with availability constraints (the bottlenecks) and upstream, and other for bottlenecks downstream. Bottlenecks schedule is driving the schedule for all other non-bottleneck machines. Makespan reduction for aggregate schedule is finally performed if possible, by left shifting scheduled operations to their earliest start times.

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2

Vyas, Sanjay R., and Dr Ved vyas dwivedi. "Reduce Power Generation Cost by Using Load Dispatch Schedule." Indian Journal of Applied Research 2, no. 2 (October 1, 2011): 66–68. http://dx.doi.org/10.15373/2249555x/nov2012/24.

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3

ZHUKOVSKY, Yuri, Alexander LAVRIK, Oleg VASILKOV, and Alexandra SEMENYUK. "POTENTIAL FOR ELECTRIC CONSUMPTION MANAGEMENT IN THE CONDITIONS OF AN ISOLATED ENERGY SYSTEM IN A REMOTE POPULATION." Sustainable Development of Mountain Territories 12, no. 4 (December 30, 2020): 583–91. http://dx.doi.org/10.21177/1998-4502-2020-12-4-583-591.

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The aim of this work is to develop approaches for a methodology creation that allows estimate preliminary assessment of the power consumption management potential in the absence of experimental data on the object under actual load study. Analysis of diesel power plants operating modes in such systems showed that diesel generators often operate in suboptimal load ranges, which leads to increased diesel fuel consumption, reduction in diesel generators service life andas a result of poor diesel fuelcombustion in the cylinders or increased mechanical parts wear, etc. At the same time, electricity demand management technology used in large centralized power supply systems in some countries of the world can be adapted for use in isolated low power systems. Considering that diesel power plants are the sources of distributed generation in the power systems of hard-to-reach facilities in mountainous areas, demand management will allow to align the daily load schedule, plan the loading, start-ups and stops of diesel generators to improve the technical, economic and environmental performance of the entire complex. This paper proposes a technique that allows estimate preliminary assessment of the object shiftable electrical load share on the basis of "base" data about a typical similar object. The initial data isthe average electrical load daily schedules for each month of the studied object, for example, a settlement. The schedule of non-shiftable partof the studied object load is constructed by scaling the base non-shiftable load schedule depending on the selected method: the series values of base schedule can be multiplied by a coefficient so that one of the base schedule values becomes equal to the corresponding series value of the studied object load schedule, and other base schedule values were less than or equal to the corresponding values. According to the second method the base schedule values can be multiplied by a coefficient so that the series values at the selected hour (for example, evening maximum) were equal to the corresponding series value of the studied object load schedule. An example of methodology application is shown to estimate the demand response potential for electricity in the Amderma settlementresidential sector in Arkhangelsk region. It also shows the calculation results of technical, economic and environmental effect of the demand side management technology, due to electric load schedule alignment and, as a result, diesel generators operationin modes close to optimal.

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4

Huang, Kai, Jingkang Wu, Xiaowen Jiang, Dongliang Xiong, Kaitian Huang, Hao Yao, Wenyuan Xu, Yonggang Peng, and Zhili Liu. "A Period-Aware Routing Method for IEEE 802.1Qbv TSN Networks." Electronics 10, no. 1 (December 31, 2020): 58. http://dx.doi.org/10.3390/electronics10010058.

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The IEEE 802.1Qbv standard provides deterministic delay and low jitter guarantee for time-critical communication using a precomputed cyclic transmission schedule. Computing such transmission schedule requires routing the flows first, which significantly affects the quality of the schedule. So far off-the-shelf algorithms like load-balanced routing, which minimize the maximum scheduled traffic load (MSTL), have been used to accommodate more time-triggered traffic. However, they do not consider that the bandwidth utilization of periodic flows is decentralized and their criteria for bottleneck of scheduling are imprecise. In this paper, we firstly explore the combinability among different periods of flows, which can measure their ability to share bandwidth without conflict. Then, we propose a novel period-aware routing algorithm to reduce the scheduling bottleneck, thus more flows can be accommodated. The experiment results show that the success rate of scheduling is significantly improved compared to shortest path routing and load balanced routing.

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5

Shaw, William George, Marc Mathews, and Johan Marais. "Holistic analysis of the effect on electricity cost in South Africa’s platinum mines when varying shift schedules according to time-of-use tariffs." Journal of Energy in Southern Africa 30, no. 4 (December 5, 2019): 26–40. http://dx.doi.org/10.17159/2413-3051/2019/v30i4a5675.

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In the past the cost of electricity was not a significant concern and was not common practice for mining companies to consider peak time-of-use (TOU) tariffs for their shift schedules. It has become more prevalent, as TOU tariffs continue increasing, to consider energy saving important. A study was carried out to analyse the mining operation of a South African deep-level platinum mine in respect of integrated load management, shift changes and TOU schedules. This was achieved by thoroughly analysing energy consumers, mine operational schedules and their interconnectedness. A specific mining system was analysed as a case study and a maximum savings scenario was determined, using the methodology formulated. The maximum savings scenario schedule change resulted in a 1.3% cost reduction. System improvements had an additional potential reduction effect of 8.4%, which was primarily the result of a reduction in compressors’ power consumption. The implications of the proposed schedule adjustments necessitated a realistic scenario. The realistic scenario had an effective financial reduction of 0.7%. The realistic schedule change, however, opened the door for large system operational improvements, which could increase the reduction potential by 7.6%. The study methods described illustrate the potential implications of integrated load management and operational schedule optimisation on the power demand and cost savings in the mining industry, specifically focusing on deep-level platinum mines.

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6

Wang, Kang Qi, Xiao Bin Tan, and Shan Jin Yu. "Load Scheduling Strategies Based on Dynamic Price in Smart Grid." Advanced Materials Research 860-863 (December 2013): 2495–500. http://dx.doi.org/10.4028/www.scientific.net/amr.860-863.2495.

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The combination of renewable energy and dynamic electricity pricing is an important feature of modern smart grid. Due to the dynamic electricity price and the intermittency nature of renewable energy, a local small-scaled smart grid (microgrid) needs to intelligently schedule various work loads to determine their start and end time in order to maximize the utilization of renewable energy and minimize the total electricity cost. Since the workload that can be shifted/scheduled is classified into interruptible load and non-interruptible load, in this paper we present two novel recursive scheduling strategies based on dynamic programming for both categories. The simulation experiment demonstrates that our strategies can effectively save energy expenditure and increase the proportion of renewable energy in overall energy consumption.

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7

Cao, Qingmei, Zhili Zhou, and Mingzhu Zhang. "Fuzzy Adaptive Shift Schedule of Tractor Subjected to Random Load." Mathematical Problems in Engineering 2017 (2017): 1–8. http://dx.doi.org/10.1155/2017/9284281.

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In this paper, the low frequency random load of a tractor is presented. Controlled by a theoretical three-parameter shift schedule, the random load would frequently trigger the random shift. Simultaneously, the driving force of the tractor should be consistent prior to and following this shift. Additionally, the higher traction efficiency and improved load utilization rate should be ensured by a choice of a transmission ratio of the tractor power shift transmission. The shift schedule was utilized for the aforementioned problems solution. An innovative method is presented for theoretical shift schedule modification by the fuzzy algorithm, based on the random load standard deviation and the alteration rate of both steady state values of the load and of the throttle position. The simulation results demonstrated that the modified shift schedule could discover the running state of the tractor. By shielding the random shift judgment caused by the random load, the stability of the tractor was ensured. When the shift was required, the schedule could rapidly respond, whereas the tractor driving force did not sustain a sudden alteration. The schedule could also automatically select and maintain the transmission ratio with higher traction efficiency.

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8

Chernichenko, K. O., A. M. Kapkanets, and T. V. Panchenko. "Contact Center Load Forecasting and Operator Schedule Planning." PROBLEMS IN PROGRAMMING, no. 2-3 (June 2016): 227–36. http://dx.doi.org/10.15407/pp2016.02-03.227.

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A non-classical method for contact center load forecasting as well as methods (Erlang C, imitation modelling) to count the necessary amount of operators have been applied. The task to predict the volume of contact center incoming calls for a certain month and also to calculate the necessary number of operators have been set and solved. Conclusions about the model’s forecast quality and precision have been presented.

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9

Muratov, Khakim, Kamoliddin Kodirov, and Alijon Kushev. "Specification of the system application for the different tariffs during the consumption time of electricity by industrial enterprises." E3S Web of Conferences 304 (2021): 01006. http://dx.doi.org/10.1051/e3sconf/202130401006.

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This article is devoted to such issues as smoothing the schedule of loads of manufacturing factories in the “intensive” periods due to the correct use of the system of tariffs, which is stratified by time. It is possible to achieve smoothing of the load schedule of the energy system through the correct use of the time-stratified tariff system by taking into account the above-mentioned issue. Proper use of the time-stratified tariff system by consumers will reduce the load period of the energy system and change its modes.

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10

Zhang, Jian Jun, Tian Hong Wang, and Yu Zhuo Wang. "Expected Cost Based Greedy Scheduling Algorithm for Out-Tree Task Graphs." Applied Mechanics and Materials 556-562 (May 2014): 3431–37. http://dx.doi.org/10.4028/www.scientific.net/amm.556-562.3431.

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Effective task scheduling is crucial for achieving high performance in heterogeneous computing environments. Whiling scheduling Out-Tree task graphs, many previous heterogeneity based heuristic algorithms usually require high scheduling costs and may not deliver good quality schedules with lower costs. Aiming at the characteristics of Out-Tree task graphs and the features of heterogeneous environments and adopting the strategy based on expected costs and task duplications, this paper proposes a greedy scheduling algorithm, which, at each scheduling step, tries to guarantee not to increase the schedule length, schedules the current task onto the used processor which minimizes its execution finish time; meanwhile, takes load balances into account to economize the use of processors. The comparative experimental results show that the proposed algorithm has higher scheduling efficiency and robust performance, which could produce better schedule which has shorter schedule length and less number of used processors.

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11

Tong, Fei, and Yuyang Peng. "A Data-Gathering, Dynamic Duty-Cycling MAC Protocol for Large-Scale Wireless Sensor Networks." Sensors 20, no. 15 (July 22, 2020): 4071. http://dx.doi.org/10.3390/s20154071.

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This paper presents a Data-gathering, Dynamic Duty-cycling (D3) protocol for wireless sensor networks. With a proposed duty-cycling MAC of high energy efficiency in D3, a routing scheme is naturally embedded to reduce protocol overhead. A packet can be forwarded in a pipelined fashion by staggering the sleep-wakeup schedules between two communicating nodes, which can significantly reduce end-to-end delay to meet real-time transmission requirements. To construct and maintain schedules, a grade and schedule establishment mechanism with a lightweight schedule error correction scheme is designed. In addition, based on the intrinsic characteristics of the network, an adaptive schedule maintenance scheme is proposed to dynamically adjust the node duty cycle to the network traffic load. The results based on the extensive OPNET simulations show that D3 can largely improve packet delivery ratio, energy efficiency and throughput, and reduce packet delivery latency.

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12

Qin, Jian Rong, and Jia Cun Wang. "Electrical Load Management Tool Design." Applied Mechanics and Materials 380-384 (August 2013): 3332–36. http://dx.doi.org/10.4028/www.scientific.net/amm.380-384.3332.

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The purpose of electrical load management in a manufacturing plant is to change the load profile in order to gain from reduced total system peak load and increased power factor. One of the widely taken approaches is to optimize the schedule of electrical equipment operation hours to take advantage of incentives and favorable pricing offered by utilities. In this paper, we present our work in designing and developing a web based tool for manufacturing plants to find out an optimal operation schedule of equipment so as to reduce energy cost. The tool allows users to configure their load by specifying electrical devices and their various parameters. Then the online system will assess power consumption over a certain time period, as well as peak power demand and power factor of the defined load.

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13

Rakotonirainy, RG, I. Durbach, and J. Nyirenda. "Considering fairness in the load shedding scheduling problem." ORiON 35, no. 2 (December 20, 2019): 127–44. http://dx.doi.org/10.5784/35-2-648.

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Every day national power system networks provide thousands of MW of electric power from generating units to consumers, requiring different operations and planning to ensure secure systems. Where demand exceeds supply, load shedding - controlled, enforced reduction in supply - is necessary to prevent system collapse. Should load shedding need to be implemented, a planned schedule is necessary to allocate geographic areas on the required period of shedding. The problem of how to construct a schedule that fairly allocates load shedding responsibilities over geographic areas with minimum economic impacts is addressed in this paper. Two programming models are proposed. The first model consists of a linear integer programming model in which the objective is to minimise the economic cost subject to different fairness allocation constraints, while the second model involves formulation of the problem as a goal programming model in which different conflicting goals are simultaneously optimised. Several case studies are conducted in the context of a realistic, but hypothetical, scenario to explore the possible solutions that the proposed models provide. Results show that a fair schedule requires a high cost whereas lower cost can only be achieved with some sacrifices to the fairness of the schedule.

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14

Elmenfy, Tawfiq H. "Gain Schedule PI Fuzzy Load Frequency Control for Two-Area Electric Power System." International Journal of Energy Optimization and Engineering 9, no. 3 (July 2020): 39–50. http://dx.doi.org/10.4018/ijeoe.2020070103.

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The use of proportional integral (PI) load frequency control (LFC) to ensure the stable and reliable operation of electric power system is practical important. Any imbalance between synchronous generators and consumption loads will cause frequency unstable within the complete power system. The purpose of the load frequency control (LFC) is to keep the power system frequency and the inter-area tie power as near equilibrium point. This article introduces a gain schedule PI fuzzy load frequency control (GLFC) applying to two area electric power system. The GLFC consists of two level control systems, where the PI controller in the conventional form and its parameters are tuned in real time by fuzzy system. A fuzzy rule base is constructed in the form set of IF-THEN that describe how to choose the PI parameters under different operating conditions. The simulation has been conducted in MATLAB Simulink package. The effectiveness of the GLFC is measured by comparison with conventional PI load frequency controller.

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15

Anishchenko, V. A., and T. V. Pisaruk. "Monitoring the Accuracy of Measurements the Load of Industrial Enterprises." ENERGETIKA. Proceedings of CIS higher education institutions and power engineering associations 63, no. 5 (October 13, 2020): 393–410. http://dx.doi.org/10.21122/1029-7448-2020-63-5-393-410.

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The normalized accuracy of the installed measuring instruments determines the scheduled measurement accuracy of the daily active load of an industrial enterprise. To maintain it during operation, syntactic and semantic methods can be used to control the reliability of load measurements. During syntactic control, the state of measuring instruments that collect and process information about the enterprise’s load is diagnosed. Semantic control is based on the use of probabilistic characteristics of the measured load. The condition necessary for semantic control is the presence of information redundancy about the values of the monitored load. It is possible to control the load limit values (settings) when a priori information is known about the lower and upper limits in which reliably measured load values in normal operation of the enterprise may be contained. The article considers control methods that use a posteriori redundant information about the dynamics of load alterations in various sections of the daily schedule. These methods include monitoring by the first increments of the load, which characterize the speed of its alterations, as well as control using linear and nonlinear extrapolations of the first increments. The results of the analysis of probabilistic characteristics of the daily active load of the enterprise and its first increments, which characterize the rate of load change, are presented. The effect of probabilistic characteristics of the first load increments on the boundaries of making a decision about the reliability of measurements is demonstrated. A comparative analysis of the effectiveness of reliability control methods based on the first load increments and their extrapolated values has been carried out on the example of measurements of daily active load schedules of the “Minsk Motor Plant” JSC.

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16

Averianova, S. P., A. A. Dubov, K. B. Kosourov, Yu M. Semchenkov, and P. E. Filimonov. "VVER-1200/1300 operation in a daily load schedule." Atomic Energy 113, no. 5 (March 2013): 305–13. http://dx.doi.org/10.1007/s10512-013-9637-7.

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17

Garcia-Guarin, Julian, David Alvarez, Arturo Bretas, and Sergio Rivera. "Schedule Optimization in a Smart Microgrid Considering Demand Response Constraints." Energies 13, no. 17 (September 3, 2020): 4567. http://dx.doi.org/10.3390/en13174567.

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Smart microgrids (SMGs) may face energy rationing due to unavailability of energy resources. Demand response (DR) in SMGs is useful not only in emergencies, since load cuts might be planned with a reduction in consumption but also in normal operation. SMG energy resources include storage systems, dispatchable units, and resources with uncertainty, such as residential demand, renewable generation, electric vehicle traffic, and electricity markets. An aggregator can optimize the scheduling of these resources, however, load demand can completely curtail until being neglected to increase the profits. The DR function (DRF) is developed as a constraint of minimum size to supply the demand and contributes solving of the 0-1 knapsack problem (KP), which involves a combinatorial optimization. The 0-1 KP stores limited energy capacity and is successful in disconnecting loads. Both constraints, the 0-1 KP and DRF, are compared in the ranking index, load reduction percentage, and execution time. Both functions turn out to be very similar according to the performance of these indicators, unlike the ranking index, in which the DRF has better performance. The DRF reduces to 25% the minimum demand to avoid non-optimal situations, such as non-supplying the demand and has potential benefits, such as the elimination of finite combinations and easy implementation.

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18

Феофанов, Александр, Aleksandr Feofanov, Михаил Шутиков, and Mikhail Shutikov. "PLANNING OF MEASURING EQUIPMENT TOGETHER WITH A MACHINE-TOOL FLEET WITH THE HELP OF MES-SYSTEM." Automation and modeling in design and management of 2019, no. 2 (June 23, 2019): 43–48. http://dx.doi.org/10.30987/article_5cf2d32c0f5a12.75882726.

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MES systems are one of the tools of The industry 4.0 concept. MES systems plan technological operations performed at certain workplaces. The main tasks solved by systems of this class include optimization, operational planning, production process management, operational recalculation of the production schedule. Mechanism used in MES-systems for calculating production schedules allows to take into account the interrelation of all elements of the operational plan, to ensure the choice of alternative technological routes and the adaptive mode of material flow management. MES allows you to simulate different production situations, for example, to calculate the optimal load of the equipment in the event of a machine failure. It allows to plan the production schedule in case of need of carrying out planned or extraordinary repairing of the equipment. The production schedule in the MES system is visualized using a Gantt chart.In the Russian Federation, MES-systems are increasingly recognized.MES systems are especially in demand in enterprises with multi-level technological processes, which depend on many factors.When implementing the MES-system in the technical control department, the data of defective products obtained by measuring any parameters on the measuring equipment are transferred to the MES-system. Next, the production schedule is recalculated in order to optimally load the machine-tool fleet. Thus, the introduction of the MES-system can improve the efficiency of the entire enterprise.

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19

Martyshkin, Viacheslav. "Power load schedule modeling and evaluation of power consumption efficiency for tunnel boring machines in subway construction V. V. Martyshkin,." Energy Safety and Energy Economy 2 (April 2022): 32–36. http://dx.doi.org/10.18635/2071-2219-2022-2-32-36.

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Tunnel boring machines used for subway construction are extremely energy-intensive. The results of power consumption evaluation for subway tunnel boring machines are presented in this paper. Methods of power load analysis were compared and a concept of a two-tiered power load schedule was created taking into account uneven power load distribution in a processing cycle. In order to compare, we have created an ideal cycle power load schedule. Based on the results, we gave certain recommendations on energy efficiency evaluation for tunnel boring machines.

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20

Higgins, Geoff. "Pitfalls of viral load testing." Microbiology Australia 31, no. 3 (2010): 119. http://dx.doi.org/10.1071/ma10119.

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Viral load testing is the quantitative measurement of viral nucleic acid in body fluids or tissues. In medical practice, viral load assays are commonly performed for HIV, Hepatitis B and Hepatitis C viruses. These assays are funded under the Medicare Benefits Schedule (MBS) system (Item numbers 69378/81/82, 69482/3 and 69488 respectively).

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21

Duarte, Vanessa Cavalcanti Paes, Ana Paula Melo, and Roberto Lamberts. "Assessing the energy performance of VAV and VRF air conditioning systems in an office building located in the city of Florianópolis." Ambiente Construído 20, no. 2 (June 2020): 261–83. http://dx.doi.org/10.1590/s1678-86212020000200399.

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Abstract The objective of this study is to analyze the energy performance of two types of water-cooled air conditioning systems, variable air volume (VAV) and variable refrigerant flow (VRF), in terms of their cooling energy use through building simulation. These systems were designed to operate in an office building located in the city of Florianópolis, Brazil. The analysis involved the application of two building use schedules: a) constant and b) variable. Moreover, an analysis of the coefficient of performance (COP) and partial load ratio (PLR), and the percentage of operating hours for each range of cooling COP and PLR for each air conditioning system, allowed the system cooling efficiency to be assessed and the results to be related to the annual energy consumption. The coefficient of performance of VAV and VRF is 6.7 and 5.0, respectively, but the VRF system presented the lowest energy consumption for both schedules. The difference in the cooling energy consumption values for the VRF and VAV systems, for the variable schedule compared with the constant schedule, is mainly influenced by the partial load performance during the hottest period of the year.

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22

Hafeez, Ghulam, Noor Islam, Ammar Ali, Salman Ahmad, and Muhammad Usman and Khurram Saleem Alimgeer. "A Modular Framework for Optimal Load Scheduling under Price-Based Demand Response Scheme in Smart Grid." Processes 7, no. 8 (August 1, 2019): 499. http://dx.doi.org/10.3390/pr7080499.

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With the emergence of the smart grid (SG), real-time interaction is favorable for both residents and power companies in optimal load scheduling to alleviate electricity cost and peaks in demand. In this paper, a modular framework is introduced for efficient load scheduling. The proposed framework is comprised of four modules: power company module, forecaster module, home energy management controller (HEMC) module, and resident module. The forecaster module receives a demand response (DR), information (real-time pricing scheme (RTPS) and critical peak pricing scheme (CPPS)), and load from the power company module to forecast pricing signals and load. The HEMC module is based on our proposed hybrid gray wolf-modified enhanced differential evolutionary (HGWmEDE) algorithm using the output of the forecaster module to schedule the household load. Each appliance of the resident module receives the schedule from the HEMC module. In a smart home, all the appliances operate according to the schedule to reduce electricity cost and peaks in demand with the affordable waiting time. The simulation results validated that the proposed framework handled the uncertainties in load and supply and provided optimal load scheduling, which facilitates both residents and power companies.

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23

Zhang, Chuan Cheng, Cui Hui Yan, Sai Dai, Dan Xu, Yi Zhu, and Wei Dong. "Research and Application of Static Security Check for Monthly Maintenance Schedule." Advanced Materials Research 1070-1072 (December 2014): 1511–15. http://dx.doi.org/10.4028/www.scientific.net/amr.1070-1072.1511.

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Monthly maintenance schedule has an important guiding significance to day ahead maintenance arrangement. However, the current management of monthly maintenance schedule is short of quantitative analysis methods. Consequently it can’t effectively take the security risk that may exist under monthly maintenance schedule. This paper presents a static security checking method for monthly maintenance schedule. First, established the monthly power grid network model based on the realtime model and device state changes, including the commissioning and decommissioning devices and maintenance devices. Next, the month-contract energy was decomposed to every day and every hour, then combined with monthly load forecast and maintenance schedule, the power system typical operation modes were generated automatically. Finally, quantitative assessment of monthly maintenance schedule was completed by power flow calculation, security analysis and evaluation indexes, including load supply adequacy and equivalent generation capacity loss. This method is instructive for monthly maintenance arrangement and its feasibility is proved by practical application.

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24

WANG, Dan, and Mao-zeng LI. "Query schedule and load shedding model in data stream system." Journal of Computer Applications 29, no. 10 (December 28, 2009): 2766–71. http://dx.doi.org/10.3724/sp.j.1087.2009.02766.

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25

Pelikán, Emil, Kryštof Eben, and Lubomír Petrák. "One-day prediction of electricity load reflecting future RCS schedule." Journal of Forecasting 15, no. 6 (November 1996): 427–35. http://dx.doi.org/10.1002/(sici)1099-131x(199611)15:6<427::aid-for635>3.0.co;2-f.

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26

Чиркова, Юлия Васильевна, and Julia Chirkova. "Machine load balancing game with linear externalities." Mathematical Game Theory and Applications 13, no. 2 (October 20, 2021): 62–79. http://dx.doi.org/10.17076/mgta_2021_2_36.

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The Machine Load Balancing Game with linear externalities is considered. A set of jobs is to be assigned to a set of machines with different latencies depending on their own loads and also loads on other machines. Jobs choose machines to minimize their own latencies. The social cost of a schedule is the maximum delay among all machines, i.e. {\it makespan. For the case of two machines in this model an Nash equilibrium existence is proven and of the expression for the Price of Anarchy is obtained.

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27

Hsiao, Yu Hsiang. "Very-Short-Term Load Forecast for Individual Household Based on Behavior Pattern Induction." Advanced Materials Research 918 (April 2014): 177–82. http://dx.doi.org/10.4028/www.scientific.net/amr.918.177.

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Electricity demand (load) forecasting has been recognizing as the key issue for achieving economic, reliable, and secure power system operation and planning. In the existing studies, two indentations are found in application level and methodology level, respectively. In the application level, the load forecasting for individual household is few. Most applications focus on large spatial region. In the methodology level, the importance of user daily schedule pattern is ignored in the development of load forecasting methods. In this study, a novel approach to model the load of individual household based on context information and daily schedule patterns is proposed. The daily schedule pattern types are induced from daily electricity consumption behaviors, and context features from several sources are collected and used to establish a rule set for classifying a given day into a specific behavior pattern type. Also, an electricity consumption volume prediction model is built for each behavior pattern type to predict the load of specific time point with in a day. A household very-short-term load forecasting problem which comes from Taiwan is implemented in this study. The results show that the proposed approach can get a better accuracy than other methods.

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28

Shaw, S. R., C. B. Abler, R. F. Lepard, D. Luo, S. B. Leeb, and L. K. Norford. "Instrumentation for High Performance Nonintrusive Electrical Load Monitoring." Journal of Solar Energy Engineering 120, no. 3 (August 1, 1998): 224–29. http://dx.doi.org/10.1115/1.2888073.

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This paper reviews the design and implementation of hardware and software tools for nonintrusive electrical load monitoring. Estimates of spectral content in measured waveforms can be used to determine in real time the operating schedule of loads at a target site. Techniques for transient event detection are reviewed. These techniques can detect the turn-on and turn-off transients of individual loads, and can be used to easily determine the energy usage of loads that draw constant power in steady-state operation. Techniques for monitoring the power consumption of smoothly varying loads (e.g., variable speed drives) using spectral estimates are also discussed.

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Lepel, A., and M. Hajdu. "Influence lines for schedule networks." IOP Conference Series: Materials Science and Engineering 1218, no. 1 (January 1, 2022): 012058. http://dx.doi.org/10.1088/1757-899x/1218/1/012058.

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Abstract An influence line graphs for project planning show how the changes of a selected activity duration affect the project duration. The name comes from the structural analysis where influence lines show on the ordinates of a graph the variation of the magnitude of, e.g., the bending moment at a specific point as a unit load traverses across the structure. This paper examines the principles of project planning influence lines: it discusses how to depict the six criticality types of activities, and it also shows what kind of transitions are possible among the criticality types. The findings help the greater understanding of the effects of the activity duration changes on the project duration.

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Barta, Gergo, Benedek Pasztor, and Venkat Prava. "Optimized Charge Controller Schedule in Hybrid Solar-Battery Farms for Peak Load Reduction." Energies 14, no. 22 (November 22, 2021): 7794. http://dx.doi.org/10.3390/en14227794.

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The goal of this paper is to optimally combine day-ahead solar and demand forecasts for the optimal battery schedule of a hybrid solar and battery farm connected to a distribution station. The objective is to achieve the maximum daily peak load reduction and charge battery with maximum solar photovoltaic energy. The innovative part of the paper lies in the treatment for the errors in solar and demand forecasts to then optimize the battery scheduler. To test the effectiveness of the proposed methodology, it was applied in the data science challenge Presumed Open Data 2021. With the historical Numerical Weather Prediction (NWP) data, solar power plant generation and distribution-level demand data provided, the proposed methodology was tested for four different seasons. The evaluation metric used is the peak reduction score (defined in the paper), and our approach has improved this KPI from 82.84 to 89.83. The solution developed achieved a final place of 5th (out of 55 teams) in the challenge.

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Jahic, Amra, Mina Eskander, and Detlef Schulz. "Charging Schedule for Load Peak Minimization on Large-Scale Electric Bus Depots." Applied Sciences 9, no. 9 (April 27, 2019): 1748. http://dx.doi.org/10.3390/app9091748.

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The city of Hamburg has decided to electrify its bus fleets. The two public transportation companies in this city expect to operate up to 1500 buses by 2030. In order to accomplish this ambitious goal, both companies need to build an appropriate charging infrastructure. They have both decided to implement the centralized depot charging concept. Buses can therefore charge only at the depot and do not have the possibility for opportunity charging at intermediate stations. The load profile of such a bus depot is highly dependent on the charging schedule of buses. Without an intelligent scheduling system, the buses charge on demand as soon as they arrive to the depot. This can lead to an unevenly distributed load profile with high load peaks, which is problematic for the local grid as well as for the equipment dimensioning at the depot. Charging scheduling on large-scale bus depots is a relatively new and poorly researched topic. This paper addresses the issue and proposes two algorithms for charging scheduling on large-scale bus depots with the goal to minimize the peak load. The schedules created with the proposed algorithms were both tested and validated in the Bus Depot Simulator, a cosimulation platform used for bus depot simulations.

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Potluri, Sirisha, and Katta Subba Rao. "Optimization model for QoS based task scheduling in cloud computing environment." Indonesian Journal of Electrical Engineering and Computer Science 18, no. 2 (May 1, 2020): 1081. http://dx.doi.org/10.11591/ijeecs.v18.i2.pp1081-1088.

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Shortest job first task scheduling algorithm allocates task based on the length of the task, i.e the task that will have small execution time will be scheduled first and the longer tasks will be executed later based on system availability. Min- Min algorithm will schedule short tasks parallel and long tasks will follow them. Short tasks will be executed until the system is free to schedule and execute longer tasks. Task Particle optimization model can be used for allocating the tasks in the network of cloud computing network by applying Quality of Service (QoS) to satisfy user’s needs. The tasks are categorized into different groups. Every one group contains the tasks with attributes (types of users and tasks, size and latency of the task). Once the task is allocated to a particular group, scheduler starts assigning these tasks to accessible services. The proposed optimization model includes Resource and load balancing Optimization, Non-linear objective function, Resource allocation model, Queuing Cost Model, Cloud cost estimation model and Task Particle optimization model for task scheduling in cloud computing environement. The main objectives identified are as follows. To propose an efficient task scheduling algorithm which maps the tasks to resources by using a dynamic load based distributed queue for dependent tasks so as to reduce cost, execution and tardiness time and to improve resource utilization and fault tolerance. To develop a multi-objective optimization based VM consolidation technique by considering the precedence of tasks, load balancing and fault tolerance and to aim for efficient resource allocation and performance of data center operations. To achieve a better migration performance model to efficiently model the requirements of memory, networking and task scheduling. To propose a QoS based resource allocation model using fitness function to optimize execution cost, execution time, energy consumption and task rejection ratio and to increase the throughput. QoS parameters such as reliability, availability, degree of imbalance, performance and SLA violation and response time for cloud services can be used to deliver better cloud services.

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Sinelnikov, D. S., and P. A. Shchinnikov. "Increasing the efficiency of the thermal power units at zoning of heat load curve." Power engineering: research, equipment, technology 21, no. 4 (December 9, 2019): 12–19. http://dx.doi.org/10.30724/1998-9903-2019-21-4-12-19.

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The article deals with the work of heat and power units of thermal power plants in the conditions of disaggregation of the heat load schedule. The purpose of the work is to increase the efficiency of operation of CHP units operating under the conditions of the zoned temperature schedule. To achieve the obtained results, mathematical modeling based on the methods of differential-exergetic and thermodynamic analysis; methods of material, energy and exergetic balances. Research has shown that in order to increase the efficiency the original method of splitting the temperature graph into three zones can be used, each of which is characterized by the method of regulation of heat supply, and to assess the effect of the minimum fuel consumption criterion can be used. The method of determining the equivalent design temperature, which takes into account the regime peculiarities of heat and power units in the form of a method of regulation of heat supply, is developed. Study has shown that three design points should be available when zoning the temperature schedule. Based on optimization calculations for standard sizes of heat and power units of a wide range of capacities, the calculations show that the optimal parameters of heat and power units in the conditions of zoning of the temperature schedule as a whole correspond to the standard values. In addition, it is shown that in each zone of the temperature schedule there is a saving of fuel, which can be from 3 to 30% depending on the type of power unit, its capacity and zone of the temperature schedule, and the annual fuel consumption of the heat and power units can be reduced by approximately 10%. It is shown that when conventional power units operate according to the zoned temperature schedule and in the first zone (quantitative regulation), preference should be given to power units with steam production. In the second zone (mixed regulation), the operation of power units with steam extraction and heat and power units is equivalent. In addition, in the third zone (qualitative regulation) preference should accrue to heat and power units.

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Hidri, Lotfi, Achraf Gazdar, and Mohammed M. Mabkhot. "Optimized Procedure to Schedule Physicians in an Intensive Care Unit: A Case Study." Mathematics 8, no. 11 (November 6, 2020): 1976. http://dx.doi.org/10.3390/math8111976.

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Hospitals are facing an important financial pressure due to the increasing of the operating costs. Indeed, the growth for the hospitals’ services demand causes a rising in the number of required qualified personnel. Enlarging the personnel number increases dramatically the fixed total cost. Based on some studies, 50% of operating costs in US hospitals are allocated to healthcare personnel. Therefore, reducing these types of costs without damaging the service quality becomes a priority and an obligation. In this context, several studies focused on minimizing the total cost by producing optimal or near optimal schedules for nurses and physicians. In this paper, a real-life physicians scheduling problem with cost minimization is addressed. This problem is encountered in an Intensive Care Unit (ICU) where the current schedule is manually produced. The manual schedule is generating a highly unbalanced load within physicians in addition to a high cost overtime. The manual schedule preparation is a time consuming procedure. The main objective of this work is to propose a procedure that systematically produces an optimal schedule. This optimal schedule minimizes the total overtime within a short time and should satisfies the faced constraints. The studied problem is mathematically formulated as an integer linear program. The constraints are real, hard, and some of them are non-classical ones (compared to the existing literature). The obtained mathematical model is solved using a state-of-the-art software. Experimental tests on real data have shown the performance of the proposed procedure. Indeed, the new optimal schedules reduce the total overtime by up to 69%. In addition, a more balanced workload for physicians is obtained and several physician preferences are now satisfied.

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Zhao, Xin Qiu. "Application of Genetic Algorithm Based on Real Number Encoding to Optimization Design for Rolling Schedule of Tandem Cold Rolling." Advanced Materials Research 383-390 (November 2011): 6014–18. http://dx.doi.org/10.4028/www.scientific.net/amr.383-390.6014.

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Rolling schedule is the core contents of rolling process. A reasonable rolling schedule can make rolling process to obtain optimum state. There are more mathematic models and the constraint conditions to be considered in the rolling schedule, therefore, the optimization becomes a complex problem. In this paper, genetic algorithms based on real number encoding are used to optimize rolling schedule aiming at equating the relative load and optimum plate shape though instance. The optimum result shows that the method is satisfactory and promising.

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Wang, Ling, Hai Bao, and Yu Long Chen. "The Selection of Online Power Flow Calculation’s Known Quantities." Advanced Materials Research 986-987 (July 2014): 1396–400. http://dx.doi.org/10.4028/www.scientific.net/amr.986-987.1396.

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The difference between schedule power flow and online power flow is that the former has no corresponding physical power flow, while the latter is the reappearance of existing physical power flow. Owing to the different premise, the selection of power flow calculation’s known quantities can be different. A schedule power flow schedules physical power flow for power system, so the selection of known quantities has strict requirements, such as active power and reactive power regulation capability. Since the physical power flow exists, the selection of online power flow calculation’s known quantities can be more flexible. For example, we can choose load bus as slack bus or replace PQ bus with PV bus. The example demonstrated that we could locate the stealing electricity buses by choosing load bus as slack bus. Considering that voltage amplitude measurement accuracy is higher than reactive power measurement accuracy, we can replace PQ bus with PV bus and make the power flow calculation results more consistent with the physical power flow. Finally, the examples show that online power flow calculation’s known quantities can be selected flexibly according to the actual situation rather than strictly following the classical power flow calculation requirements.

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Kampo, Mohamed, Babacar Mbaye Ndiaye, and Guy Degla. "Dynamic Programming for an Optimal and Equitable Public Load Shedding Schedule." Croatian Operational Research Review 9, no. 2 (December 13, 2018): 211–21. http://dx.doi.org/10.17535/crorr.2018.0016.

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TAKAGI, Akiyoshi, Shinichi MUTOH, and Takahiro KAMIMURA. "Efficient schedule of reduced pollutant load countermeasures for water quality improvement." ENVIRONMENTAL SYSTEMS RESEARCH 29 (2001): 47–55. http://dx.doi.org/10.2208/proer.29.47.

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Yang, Peng, Phani Chavali, Elad Gilboa, and Arye Nehorai. "Parallel Load Schedule Optimization With Renewable Distributed Generators in Smart Grids." IEEE Transactions on Smart Grid 4, no. 3 (September 2013): 1431–41. http://dx.doi.org/10.1109/tsg.2013.2264728.

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Aldwin, Dionisius, Fran Setiawan, and Loren Pratiwi. "Developing Bus Rapid Transit’s Schedule using Max Load Method (Trans Metro Bandung Corridor 2 Case Study)." Jurnal Ilmiah Teknik Industri 20, no. 2 (December 31, 2021): 221–32. http://dx.doi.org/10.23917/jiti.v20i2.15639.

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Congestion is one of problems that must be solved by the Government of Bandung City. One of the projects are improving the public transport sector. Trans Metro Bandung (TMB) is one of public transport at Bandung. People’s interest to use TMB is very low. This happens because TMB does not have a reliable bus schedule. The focus of this study is to build a TMB’s corridor 2 schedule with headway based on the current passenger condition. The headway is determined using the max load method. This study will compare the current headway target set by TMB with the headway based on the passenger arriving pattern. After that, the schedule which is made from the choosen headway will be simulated. This study shows that the current headway set by Trans Metro Bandung management is better. Simulation shows that schedule can reduce passenger average waiting time quite significant.

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Stadnik, Mykola, Aatolii Hrytsun, and Dmytro Protsenko. "METHOD OF COVERING THE SCHEDULE OF LOADING OF A LIVESTOCK FARM IN THE CASE OF AUTONOMOUS ELECTRICITY SUPPLY, BASED ON BIOGAS PLANTS." ENGINEERING, ENERGY, TRANSPORT AIC, no. 4(107) (December 20, 2019): 79–87. http://dx.doi.org/10.37128/2520-6168-2019-4-10.

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The paper proposes a method of tracking the livestock farm load schedule based on the determination of the required generation capacity depending on the actual load schedule. The use of this method allows the management of biogas power plants based on internal combustion engines, depending on changes in the load without power reserve for the time of possible displacement of technological operations. Unlike the power backup method, there is no additional power loss at the time of the reservation, the possible incorrect switches of electrical equipment that do not correspond to the normalized schedule are excluded, there is work in changing the order of operations and the order of switching on the equipment. The use of the method improves the energy efficiency of the biogas plant and also increases the reliability of the livestock farm's autonomous electricity supply system. The calculations of the use of this method for the cattle farm prove its energy efficiency in the conditions of autonomous electricity supply. Developed approaches to tracking the load schedule of the livestock farm allow for the management of biogas power plants depending on changes in the magnitude of the load without power reserve for the time of possible displacement of technological operations. The use of the proposed method is especially relevant for autonomous power systems. The calculations of using this method for a cattle farm testify to its effectiveness, in particular for a small cattle farm with a herd of 60 valid cows, 11 dry and 29 heads of young animals kept in the winter-stall period, the total saving is 82.8 kW ∙ h day.

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Yoon, Yourim, and Yong-Hyuk Kim. "Charge Scheduling of an Energy Storage System under Time-of-Use Pricing and a Demand Charge." Scientific World Journal 2014 (2014): 1–9. http://dx.doi.org/10.1155/2014/937329.

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A real-coded genetic algorithm is used to schedule the charging of an energy storage system (ESS), operated in tandem with renewable power by an electricity consumer who is subject to time-of-use pricing and a demand charge. Simulations based on load and generation profiles of typical residential customers show that an ESS scheduled by our algorithm can reduce electricity costs by approximately 17%, compared to a system without an ESS and by 8% compared to a scheduling algorithm based on net power.

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Bahadori-Jahromi, Ali, Radwa Salem, Anastasia Mylona, Agha Usama Hasan, and Hexin Zhang. "The Effect of Occupants’ Behaviour on the Building Performance Gap: UK Residential Case Studies." Sustainability 14, no. 3 (January 25, 2022): 1362. http://dx.doi.org/10.3390/su14031362.

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Studies have shown that the assumptions used to create dynamic thermal models of buildings do not reflect their actual energy use. Bridging the energy performance gap is vital in ensuring that a designed or retrofitted building meets the energy performance targets. Using thermal analysis simulation software TAS, this paper presents a simulation model of seven different UK single family houses. The results from the various models are validated by comparing the actual energy demand against the simulated consumption. The simulation results show that the heating set point has the greatest impact on the simulated energy demand. The results also demonstrate that the energy demand of the dwellings can be reduced by applying window opening schemes and by controlling the heating setpoint temperature and schedule. Plug load consumption is also considered by using plug load data of real UK households, as obtained from a longitudinal study, and calibrating the model based on average plug load contributions for the households. The results showed that, by increasing the heating set point and window opening schedules by 10% from self-reported data, and by considering an additional 12% for plug loads, the energy performance gap is reduced to less than >15% for all examined houses.

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AZEMSHA, Siarhei, Irina KRAVCHENYA, Yuriy VOVK, Oleg LYASHUK, and Iryna VOVK. "SCHEDULING TECHNIQUE OF ROUTE VEHICLES ON DUPLICATING STRETCHES." Scientific Journal of Silesian University of Technology. Series Transport 113 (December 1, 2021): 5–16. http://dx.doi.org/10.20858/sjsutst.2021.113.1.

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Schedule optimization is a proven strategy to improve service quality for public transport networks. However, current research mostly optimizes schedule design using prior knowledge of users’ routings, ignoring the optimization of public transport schedules on duplicating stretches of route vehicles. This article presents a new alignment technique of time intervals between consecutive vehicles of different routes on duplicating stretches, considering existing public transport networks in attaining optimization of public transport schedule. Scheduling technique of route vehicles on duplicating stretches includes some steps: analysis of public transport network and determining a lot of duplicating stretches, calculation of the optimal time intervals among arrivals of route vehicles and alignment of these intervals among consecutive route vehicles on duplicating stretches, realization simulation model of urban passenger transport within the simulation modeling system of GPSS World. Furthermore, testing the optimization technique of route vehicle scheduling, analysis of the quality of adjusted schedule with route vehicles of different kinds included, and determining the optimization efficiency for duplicating stretches. The adjusting technique of urban passenger transport schedule allows to increase movement steadiness of consecutive vehicles of different routes on duplicating stretches, adjust traffic intervals for each route, shorten the traffic load on stations, reducing idle time and queue lengths of route vehicles in front of transport stops and also minimize waiting time for route vehicle by those passengers who can be transported along several routes. Improving the bus schedule on duplicating stretches in Gomel is illustrated demonstrating the technique developed. This scheduling technique can be used by carriers and transport operators to improve the quality of services provided.

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Wei, Li Feng, Jian Wei Ji, and Li Qiang Zhao. "The Research and Design of Two-Level Load Balancer Based on Web Server Cluster." Advanced Materials Research 282-283 (July 2011): 765–69. http://dx.doi.org/10.4028/www.scientific.net/amr.282-283.765.

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At present, the extendible Web server clusters are the important framers for the high performance network servers. The architecture based on load balancer is often used on web server clusters. The load balancer is the main bottleneck in improving performance of cluster. For it is hard to complete the task of assigning the requests from the client while collecting the real load information of the web servers efficiently. In this paper, we propose a model of two levels load balancer for web cluster to resolve the contradiction. When web requests are arrive, the first-level balancer continuously assigned them by to the second-level which consist of some load balancers connect to real web servers and receive real-time load information of them. Round robin algorithm is used at the first-level load balancer to schedule the client’s requests in order to shorten the response time. The proposed algorithm in our model is run at the second-level load balancers to schedule the requests to real web servers according to the real load information. At last, a simple test is carried out to make sure the performance of the model. The result of the test shows that the model has made better load balancing.

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Nabiullin, D. I., and R. N. Balobanov. "Prediction of the electrical load of the power system using neural networks." E3S Web of Conferences 124 (2019): 05026. http://dx.doi.org/10.1051/e3sconf/201912405026.

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Prediction of the electrical load schedule of an electrical system is an important aspect for determining electrical loads, which ensures the correct selection and cost-effective operation of reactive power compensation devices and voltage control devices, as well as relay protection and automation. This article discusses methods for predicting electrical load using an artificial neural network. The problems of choosing the optimal architecture and algorithm of neural network training are considered. The methods of the best forecast accuracy are determined. A genetic algorithm based on the group method of data handling was chosen as the main calculation.

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Ullah, Ibrar, Irshad Hussain, and Madhusudan Singh. "Exploiting Grasshopper and Cuckoo Search Bio-Inspired Optimization Algorithms for Industrial Energy Management System: Smart Industries." Electronics 9, no. 1 (January 6, 2020): 105. http://dx.doi.org/10.3390/electronics9010105.

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Industries are consuming more than 27% of the total generated energy in the world, out of which 50% is used by different machines for processing, producing, and assembling various goods. Energy shortage is a major issue of this biosphere. To overcome energy scarcity, a challenging task is to have optimal use of existing energy resources. An efficient and effective mechanism is essential to optimally schedule the load units to achieve three objectives: minimization of the consumed energy cost, peak-to-average power ratio, and consumer waiting time due to scheduling of the load. To achieve the aforementioned objectives, two bio-inspired heuristic techniques—Grasshopper-Optimization Algorithm and Cuckoo Search Optimization Algorithm—are analyzed and simulated for efficient energy use in an industry. We considered a woolen mill as a case study, and applied our algorithms on its different load units according to their routine functionality. Then we scheduled these load units by proposing an efficient energy management system (EMS). We assumed automatic operating machines and day-ahead pricing schemes in our EMS.

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Mayilsamy, Jayaprakash, and Devi Priya Rangasamy. "Enhanced Routing Schedule - Imbalanced Classification Algorithm for IOT based Software Defined Networks." International Academic Journal of Science and Engineering 8, no. 1 (February 1, 2021): 01–09. http://dx.doi.org/10.9756/iajse/v8i1/iajse0801.

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Route scheduling optimization is important in SDN network. The SDN network needs the best solution for route optimization. Limited networking of software is the most interesting development in this field as it is important to provide a fast and reliable routing path based on its need. The IoT supports software defined applications interface in the overall networks. The SDN is recommended by enhancing the SDN architecture's benefits in improving research network quality. SDN network information exchange is one of the most important factor. It is important to plan the information accordingly and adjust a load of information to the SDN. A Maximum throughput scheduling process is proposed, which is upgraded using the Imbalanced Classification Algorithm. SDN has shown the advantage in many ways compared to the traditional network. But the problem of load inconstancy still occurs in SDN. The imbalanced classification method supports the maximum throughput schedule function and integrates load balancing strategies to improve SDN networks' Performance. Classification is to be proposed based on machine command in QoS. An imbalanced classification learning method is used for improving the QoS requirements and shows that the simulated results of the identified traffic load balance and maximum throughputs in the proposed solutions. Functionality has been improved much better than previous functions in the same area.

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Joy, Vijo M., and S. Krishnakumar. "Optimal design of adaptive power scheduling using modified ant colony optimization algorithm." International Journal of Electrical and Computer Engineering (IJECE) 10, no. 1 (February 1, 2020): 738. http://dx.doi.org/10.11591/ijece.v10i1.pp738-745.

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For generating and distributing an economic load scheduling approach, artificial neural network (ANN) has been introduced, because power generation and power consumption are economically non-identical. An efficient load scheduling method is suggested in this paper. Normally the power generation system fails due to its instability at peak load time. Traditionally, load shedding process is used in which low priority loads are disconnected from sources. The proposed method handles this problem by scheduling the load based on the power requirements. In many countries the power systems are facing limitations of energy. An efficient optimization algorithm is used to periodically schedule the load demand and the generation. Ant colony optimization (ACO) based ANN is used for this optimal load scheduling process. The present work analyse the technical economical and time-dependent limitations. Also the works meets the demanded load with minimum cost of energy. Inorder to train ANN back propagation (BP) technics is used. A hybrid training process is described in this work. Global optimization algorithms are used to provide back propagation with good initial connection weights.

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Mulyono, Mulyono. "Implementasi Demand Side Management (DSM) Pada Instalasi Pengolahan Air PDAM Mulia Baru." Energi & Kelistrikan 12, no. 1 (June 30, 2020): 43–52. http://dx.doi.org/10.33322/energi.v12i1.934.

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The Mulia Baru Water Treatment Plant (IPA) is one of 6 Water Treatment Plants owned by the Ketapang Regency PDAM that is indicated to use a large amount of electrical energy. From the data obtained during one year (January to December 2017), the average monthly electricity consumption is 128,016,667 kWh with an average.Drinking Water Production Volume per month of 144,119,833 m3. Therefore the average Specific Energy Consumption (SEC) value for 2017 is 0.89. Through the application of the Peak Clipping and Strategic Concervation DSM program, a decrease in peak load occurs at a time interval between 17:00 and 22:00, in this condition a decrease in load of 0.47 (%).For greater savings, it is necessary to schedule the operation of large loads at peak load times.

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