Forecasting planned electricity consumption for the united power system using machine learning

The paper presents the results of studies of the predictive models development based on retrospective data on planned electricity consumption in the region with a significant share of enterprises in the mineral resource complex. Since the energy intensity of the industry remains quite high, the task of rationalizing the consumption of electricity is relevant. One of the ways to improve control accuracy when planning energy costs is to forecast electrical loads. Despite the large number of scientific papers on the topic of electricity consumption forecasting, this problem remains relevant due to the changing requirements of the wholesale electricity and power market to the accuracy of forecasts. Therefore, the purpose of this study is to support management decisions in the process of planning the volume of electricity consumption. To realize this, it is necessary to create a predictive model and determine the prospective power consumption of the power system. For this purpose, the collection and analysis of initial data, their preprocessing, selection of features, creation of models, and their optimization were carried out. The created models are based on historical data on planned power consumption, power system performance (frequency), as well as meteorological data. The research methods were: ensemble methods of machine learning (random forest, gradient boosting algorithms, such as XGBoost and CatBoost) and a long short-term memory recurrent neural network model (LSTM). The models obtained as a result of the conducted studies allow creating short-term forecasts of power consumption with a fairly high precision (for a period from one day to a week). The use of models based on gradient boosting algorithms and neural network models made it possible to obtain a forecast with an error of less than 1 %, which makes it possible to recommend the models described in the paper for use in forecasting the planned electricity power consumption of united power systems

Improving the efficiency of relay protection at a mining and processing plant

The paper presents the results of constructing effective relay protection in the power supply system of a mining and processing plant (MPP). A brief description of the MPP is given, the power supply and substitution circuits used to calculate the short-circuit currents are given. A statistical analysis of failures in the electric network of the MPP has been carried out, which makes it possible to draw conclusions about the nature of failures ranges. Analysis of the registered faults shows that a significant part of them are line-to-earth faults, which in most cases turn into multiphase short circuits, which are interrupted by overcurrent protection. In order to improve the efficiency and reliability of the relay protection, the power supply scheme of the MPP was refined and analyzed. The calculation of the short-circuit currents was made, which made it possible to calculate the settings of the relay protection and give recommendations on the place of its installation and adjustment in order to ensure the normal operation of electricity consumers. To reduce the number of failures to the cable insert on the line leaving the administrative and household complex (AHC), and to increase the reliability of power supply to consumers, it is advisable to divide the capacities of the existing 10 kV line into two parallel ones by laying a second line. It is recommended to install a current cut-off on the line outgoing to the AHC, the feasibility of the installation of which was shown by calculations. This will reduce the chance of failures to the cable gland. Data on the setting currents of overcurrent protection and current cut-off are given on the selectivity card

System analysis of power consumption by nonferrous metallurgy enterprises on the basis of rank modeling of individual technocenosis castes

To increase energy efficiency at non-ferrous metallurgy enterprises, an integrated system approach for estimation of electricity consumption is needed. The paper presents the results of a rank analysis of the power consumption of individual castes of process equipment on the basis of an integrated energy survey of the enterprise. A methodology for constructing mathematical models for calculating and predicting electric power consumption for all castes of the ranked H-distribution of technocenosis has been developed. For the first time, according to the established regularity of the H-distribution, a mathematical model for predicting power consumption has been developed, including a quantitative analysis of the energy characteristics of consumers by individual castes of technocenosis. A retrospective check of the relative error in the prediction of electricity consumption showed that for the model it does not exceed 2%, which is significantly lower than the relative error of the prediction for a number of models of other types. The received model is recommended for use in the automated system of dispatching control of power consumption for the purposes of short-term forecasting of electric power consumption at industrial enterprises of non-ferrous metallurgy

Methods of Forecasting Electric Energy Consumption: A Literature Review

Balancing the production and consumption of electricity is an urgent task. Its implementation largely depends on the means and methods of planning electricity production. Forecasting is one of the planning tools since the availability of an accurate forecast is a mechanism for increasing the validity of management decisions. This study provides an overview of the methods used to predict electricity supply requirements to different objects. The methods have been reviewed analytically, taking into account the forecast classification according to the anticipation period. In this way, the methods used in operative, short-term, medium-term, and long-term forecasting have been considered. Both classical and modern forecasting methods have been identified when forecasting electric energy consumption. Classical forecasting methods are based on the theory of regression and statistical analysis (regression, autoregressive models); probabilistic forecasting methods and modern forecasting methods use classical and deep-machine-learning algorithms, rank analysis methodology, fuzzy set theory, singular spectral analysis, wavelet transformations, Gray models, etc. Due to the need to take into account the specifics of each subject area characterizing an energy facility to obtain reliable forecast results, power consumption modeling remains an urgent task despite a wide variety of other methods. The review was conducted with an assessment of the methods according to the following criteria: labor intensity, requirements for the initial data set, scope of application, accuracy of the forecasting method, the possibility of application for other forecasting horizons. The above classification of methods according to the anticipation period allows highlights the fact that when predicting power consumption for different time intervals, the same methods are often used. Therefore, it is worth emphasizing the importance of classifying the forecast over the forecasting horizon not to differentiate the methods used to predict electricity consumption for each period but to consider the specifics of each type of forecasting (operative, short-term, medium-term, long-term)