The possibility of using an artificial neural network for the short-term and operational prediction of the load of an electric power system is considered. A structure of an artificial neural network, which predicts its "own" hour is proposed, and the optimum set of input data is determined. The advantages of using this method compared with other methods employed at the present time are proved using the example of the Samara power system.Keywords: artificial neural network, load prediction, outside air temperature, power system. Short-term and operational load prediction plays a key role in ensuring the economic and safe operation of a power system. The choice of the structure of the generating equipment, the planning of the dispatcher graph and the estimate of the power plant stability require solutions of this problem.The load graph of the majority of consumers possesses the property of quasisimilarity: the consumption during the last period, as a rule, is similar to the consumption in the previous periods. The main reasons for the difference in the load as a function of the measurement period (the hour, day, month and year) are as follows:-load dispersion -the change in the mode of operation of major consumers; -a change in the composition (the connection of new and the disconnection of old) or parameters of the consumers;-the effect of meteorological factors (the temperature or illumination), since a considerable portion of the load is used to maintain normal conditions in buildings (heating, ventilation and air conditioning).The solution of the problems of short-term and operational prediction of the load is based on the use of the following model [1]:where P (t ) is the power consumption per 1 h t, F (t ) are the trend values (the hour before, the days before, the months before etc.), which have the greatest influence on the predicted value, K (t ) is a correction function, which takes into account the dispersion, the change in the loads and the temperature of the outside air, and k and m are the values of the time shift, determined by the trend values that have the greatest effect on the predicted value. Regression methods are employed to solve problems of short-term and operational load prediction. However, they are based on linear models, while the load series which they model are nonlinear functions of exogenic variables. Moreover, regression methods do not enable the effect of meteorological factors on the value of the predicted load to be taken into account accurately in view of the nonlinearity of this effect. The change in the load structure, which is occurring at the present time (the considerable increase in the portion of communal-everyday and nonindustrial load), means that meteorological factors have a considerable influence on it [2].With the development of the theory of artificial intelligence, it has been suggested that the problem of predicting an electrical load can be solved using models based on artificial neural networks (ANN). The theory of artificial intelligence assumes the existence ...

Objective: Existing studies estimate that between 0.3% and 2% of adults in the U.S. (between 900,000 and 2.6 million in 2020) identify as a nonbinary gender or otherwise gender nonconforming. In response to the RDAP 2021 theme of radical change, this article examines the need to change how datasets represent nonbinary persons and how research involving gender data should approach the curation of this data at each stage of the research lifecycle. Methods: In this article, we examine some of the known challenges of gender inclusion in datasets and summarize some solutions underway. Using a critical lens, we examine the difference between current practice and inclusive practice in gender representation, describing inclusive practices at each stage of the research lifecycle from writing a data management plan to sharing data. Results: Data structures that limit gender to “male” and “female” or ontological structures that use mapping to collapse gender demographics to binary values exclude nonbinary and gender diverse populations. Some data collection instruments attempt inclusivity by adding the gender category of “other,” but using the “other” gender category labels nonbinary persons as intrinsically alien. Inclusive change must go farther, to move from alienation to inclusive categories. We describe several techniques for inclusively representing gender in data, from the data management planning stage, to collecting data, cleaning data, and sharing data. To facilitate better sharing of gender data, repositories must also allow mapping that includes nonbinary genders explicitly and allow for ontological mapping for long-term representation of diverse gender identities. Conclusions: A good practice during research design is to consider two levels of critique in the data collection plan. First, consider the research question at hand and remove unnecessary gendering from the data. Secondly, if the research question needs gender, make sure to include nonbinary genders explicitly. Allies must take on this problem without leaving it to those who are most affected by it. Further, more voices calling for inclusionary practices surrounding data rises to a crescendo that cannot be ignored.

Objective: This paper compares the pedagogical theory driving current norms towards instruction of novices in both fields, specifically focusing on The Carpentries and ACRL Framework instruction. I identify key areas of difference in theoretical and practical approaches towards education of learners entirely new to a topic, focusing on a choice to pursue constructivist or experiential learning versus providing direct instructional guidance. Methods:Two case studies are explored through the lens of the Dreyfus Model of learning for their theoretical underpinnings for engaging novice learners: the ACRL Framework and Carpentries' Instructor Training. Results:Applying the Dreyfus Model of learning and cognitive load theory shows theoretical benefits to direct instructional guidance over constructivist or minimally guided instruction. Conclusions:The ACRL Framework and Carpentries workshops share teaching goals of creating new mental models and core skills to support future learning, but differ in their pedagogical approaches. For novice learners of information literacy, there may be value in considering a more guided approach. Concrete lesson-planning strategies are proposed.

Состояние вопроса. Научная проблема исследования заключается в необходимости прогнозирования электропотребления собственных нужд электростанций с минимальной ошибкой. Решением задач краткосрочного прогнозирования ранее занимались на уровне электроэнергетических систем и промышленных предприятий. Что касается прогнозирования электропотребления собственных нужд электростанций, то в качес тве прогнозных значений использовались ретроспективные данные по электропотреблению. Данная проблема сохраняет свою актуальность согласно Постановлению Правительства РФ от 27 декабря 2010 г. № 1172, в котором отм ечено, что электростанции берут на себя ответственность за потребление электроэнергии, объем которого вышел за рамки установленного. Отклонение в электропотреблении на 2 % и более от установленного значения приводит к дополнительным финансовым расходам. В связи с этим актуальным является выбор метод а прогнозирования электропотребления собственных нужд ТЭЦ с низкой погрешностью. Материалы и методы. Для решения задач краткосрочного прогнозирования выбран метод, основанный на искусственных нейронных сетях, и проведено обучение данных сетей с помощью методов численной оптимизации: алгоритма обучения Бройдена-Флетчера-Гольдфарба-Шанно; метода Сопряженных градиентов; метода градиентного спуска, которые практически использовались для решения различных задач в электроэнергетике. Для определения почасовых значений электрической нагрузки собственных нужд ТЭЦ использован программный пакет Statistica Neural Networks. Результаты. Выбран метод, основанный на искусственных нейронных сетях «многослойный персептрон» и определен алгоритм его обучения Бройдена-Флетчера-Гольдфарба-Шанно, с помощью которого на ТЭЦ появляется возможность прогнозировать электропотребление системой собственных нужд со средней абсолютной погрешностью 0,43 %. Выводы. Предложенная методика краткосрочного прогнозирования электропотребления СН ТЭЦ протестирована и утверждена в Филиале АО «СО ЕЭС» ОДУ Средней Волги для оценки прогнозных значений электропотребления электростанций в процессе планирования баланса электроэнергии. Ключевые слова: прогнозирование электропотребления, собственные нужды ТЭЦ, искусственные нейронные сети, алгоритмы обучения, ошибка прогноза

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