Boyarkin D.A., Krupenev D.S., Iakubobsky D.V. Prediction of the power shortage in the electric power system by means of regression analysis by machine learning methods // E3S Web of Conferences. Vol.114. ID: 03003. 2019. DOI: 10.1051/e3sconf/201911403003 Modern electricity consumers place increasingly high demands on the level of reliability of power supply and, correspondingly,...
Теги: decision trees , electric power systems , machine learning , number theory , random number generation , regression analysis , software reliability , support vector machines , electric power systems (eps) , macAlekseev A.V. Study of the forecasting problem of energy consumption of water pumping station // E3S Web of Conferences. Vol.102. ID: 03001. 2019. DOI: 10.1051/e3sconf/201910203001 The article describes the study of the energy consumption forecasting of city water pumping station. The review of the existing approaches for technical systems energy consumption forecasting is made. The shot description of the studied object properties including hourly energy consumption is presented. Two often used...
Теги: energy utilization , hydraulic equipment , pipelines , pumping plants , regression analysis , exponential smoothing , forecasting methods , forecasting problems , moving average method , object property , technKhamisov O.V. Algebraic solution of the problems of nonconvex quadratic programming // Automation and Remote Control. Vol.65. No.2. 2004. P.218-226. DOI: 10.1023/B:AURC.0000014718.51686.38 An algebraic approach founded on the Gröbner bases coupled with the necessary optimality conditions of the first order was used to solve the problem of nonconvex quadratic programming. Illustrative numerical examples were presented.
Теги: algebraic solutions , linear functions , market relations , quadratic functions , algebra , computer simulation , functions , matrix algebra , problem solving , regression analysis , set theory , quadratic progr
