Improving of the Generation Accuracy Forecasting of Photovoltaic Plants Based on k-Means and k-Nearest Neighbors Algorithms

Renewable energy sources (RES) are seen as a means of the fuel and energy complex carbon footprint reduction but the stochastic nature of generation complicates RES integration with electric power systems. Therefore, it is necessary to develop and improve methods for forecasting of the power plants generation using the energy of the sun, wind and water flows. One of the ways to improve the accuracy of forecast models is a deep analysis of meteorological conditions as the main factor affecting the power generation. In this paper, a method for adapting of forecast models to the meteorological conditions of photovoltaic stations operation based on machine learning algorithms was proposed and studied. In this case, unsupervised learning is first performed using the k-means method to form clusters. For this, it is also proposed to use studied the feature space dimensionality reduction algorithm to visualize and estimate the clustering accuracy. Then, for each cluster, its own machine learning model was trained for generation forecasting and the k-nearest neighbours algorithm was built to attribute the current conditions at the model operation stage to one of the formed clusters. The study was conducted on hourly meteorological data for the period from 1985 to 2021. A feature of the approach is the clustering of weather conditions on hourly rather than daily intervals. As a result, the mean absolute percentage error of forecasting is reduced significantly, depending on the prediction model used. For the best case, the error in forecasting of a photovoltaic plant generation an hour ahead was 9 %. © 2023 Belarusian National Technical University. All rights reserved

Stimulating effect of double-stranded yeast RNA on the activity of interferon system genes

Influence of double-stranded RNA (dsRNA) from Saccharomyces cerevisiae yeast upon expression levels of the macrophage genes encoding TLR3 receptor, interferons alpha and beta (IFNα, IFNβ), 2’,5’-oligoadenylate synthetase (OAS) and protein kinase R (PKR) enzymes has been studied in the J774 mouse histiocytic cell culture and in vivo in Balb/c mice. It has been shown that dsRNA exerts a selective activating effect on genes of TLR3 receptor, antiviral proteins IFNα, IFNβ, and OAS, both in vitro and in vivo. With J774 cell culture, the highest induction capacity was observed for the IFNβ gene: 365 to 802-fold. The stimulatory effect was dependent on the dose of dsRNA in the range of 16.9 to 125 μg/ml. The preparation enhanced IFNα gene activity to lesser degree (more than 10-fold), TLR3 and OAS (3 to 4-fold), while the expression levels for these genes were not significantly dependent on the dose of dsRNA. The stimulating effect of dsRNA was dosedependent in murine peritoneal macrophages. The maximum activating effect of the preparation was shown upon administration of the effective antiviral dose (0.5 mg of dsRNA/kg). Five hours after intraperitoneal injection of dsRNA, the highest level of mRNA synthesis was observed for IFNα (54-fold), OAS (43-fold) and TLR3 (28-fold) genes. Expression of the IFNβ gene increased to a lesser degree (9-fold). An increase in the dose of preparation to 1.5 mg/kg led to decrease of the stimulatory effect. Expression levels of the IFNα, TLR3, and OAS genes in that case decreased by 2-4-fold as compared to a lower dose, and the PKR gene expression was 5-fold lower compared to the control. One day after dsRNA administration, a tendency was observed for both experimental groups towards a decreased transcription of macrophage genes, if compared with the 5-hour term. The weakening of gene activity was less pronounced in animals treated with dsRNA at the dose of 1.5 mg/kg. The transcription indices for IFNβ, OAS, and TLR3 genes were much higher during this period (5-10-fold higher than the control values). The dynamics of PKR gene transcription in both experimental systems was significantly different from the expression of other studied genes. The dsRNA preparation at this dose range did not have a pronounced stimulatory effect upon expression of this gene. A moderate increase in PKR gene activity in macrophages of mice was observed only a day following intraperitoneal administration of dsRNA. Concentrations and length of dsRNA molecules are known to be critical factors to the PKR gene activation. An ability to increase the expression of the gene is shown at low dsRNA concentrations (10-7 g/ml and below), while highly polymeric dsRNAs weaken the gene activity. Since the doses and concentrations of dsRNA used in our experiments were significantly different from those mentioned above, it could, in general, affect regulation of PKR gene transcription towards reduction of the stimulatory effect

MEDICINAL FORM OF TNF-α FOR LOCAL ADMINISTRATION

Composite preparation of tumor necrosis factor alpha and rheopolyglukin and polyethylene glycol (TNF-α+PG+PEG) was obtained. The specific activity of the samples was 4,13 х 107 IU/mg. The cytolytic activity of drugs TNF-α+PG+PEG and rhTNF-α did not change after 4 months when stored at 6 °С. Preparation TNF-α+PG+PEG provided a moderately prolonged elevation of TNF-alpha in blood of laboratory mice in contrast to TNF-α when they applied to the skin. The composite preparation did not have toxic, allergic and locally irritating action in experiments on laboratory animals

Повышение точности прогнозирования генерации фотоэлектрических станций на основе алгоритмов k-средних и k-ближайших соседей

Renewable energy sources (RES) are seen as a means of the fuel and energy complex carbon footprint reduction but the stochastic nature of generation complicates RES integration with electric power systems. Therefore, it is necessary to develop and improve methods for forecasting of the power plants generation using the energy of the sun, wind and water flows. One of the ways to improve the accuracy of forecast models is a deep analysis of meteorological conditions as the main factor affecting the power generation. In this paper, a method for adapting of forecast models to the meteorological conditions of photovoltaic stations operation based on machine learning algorithms was proposed and studied. In this case, unsupervised learning is first performed using the k-means method to form clusters. For this, it is also proposed to use studied the feature space dimensionality reduction algorithm to visualize and estimate the clustering accuracy. Then, for each cluster, its own machine learning model was trained for generation forecasting and the k-nearest neighbours algorithm was built to attribute the current conditions at the model operation stage to one of the formed clusters. The study was conducted on hourly meteorological data for the period from 1985 to 2021. A feature of the approach is the clustering of weather conditions on hourly rather than daily intervals. As a result, the mean absolute percentage error of forecasting is reduced significantly, depending on the prediction model used. For the best case, the error in forecasting of a photovoltaic plant generation an hour ahead was 9 %.Возобновляемые источники энергии рассматриваются как средство снижения углеродного следа топливно-энергетического комплекса, при этом стохастический характер генерации осложняет их интеграцию с электроэнергетическими системами. Эта существенная трудность обусловливает необходимость создавать и совершенствовать методы прогнозирования генерации электрических станций, использующих энергию солнца, ветра и водных потоков. Наиболее важным направлением, обеспечивающим повышение точности прогнозных моделей, является глубокий анализ метеорологических условий как главного фактора, влияющего на выработку электроэнергии. В данной работе предложен и исследован метод адаптации прогнозных моделей под метеорологические условия работы фотоэлектрических станций на базе алгоритмов машинного обучения. При этом вначале выполняется обучение без учителя методом k-средних для формирования кластеров. Для этой задачи также предложено и исследовано использование алгоритма понижения размерности пространства признаков для визуализации оценки точности кластеризации. Затем для каждого кластера построена своя модель машинного обучения для формирования прогнозов и алгоритм k-ближайших соседей для отнесения текущих условий на этапе эксплуатации модели к одному из сформированных кластеров. Исследование было проведено на почасовых метеорологических данных за период с 1985 по 2021 г. Одной из особенностей этого подхода является кластеризация метеоусловий на часовых, а не суточных интервалах. В результате средний модуль относительной ошибки прогнозирования существенно снижается в зависимости от используемой модели прогнозирования. Для наилучшего варианта ошибка прогнозирования генерации фотоэлектрической станции на час вперед составила 9 %

Фармакокинетика рекомбинантного человеческого фактора некроза опухоли альфа в составе средства доставки

The main problems of using TNF-alpha in antitumor therapy are its rapid degradation in the bloodstream and the limited selectivity of accumulation in the tumor tissue. The SRC VB «Vector» developed a biodegradable molecular construct that provides protection against proteases and ensures targeted delivery of proteins to the tumor tissue. This construct was used to create an antitumor drug containing recombinant human TNF-alpha (rhTNF-alpha).The aim of the study was to analyse rhTNF-alpha pharmacokinetics in the delivery system after a single administration.Materials and methods: the rhTNF-alpha drug carried by the delivery system was intravenously administered to female outbred ICR (СD-1) mice only once at two effective antitumor doses, 2.55 μg and 5.1 μg / 20 g of body weight. The concentration of TNF-alpha in the serum and supernatants of organ homogenates, obtained at different time points after administration, was analysed by immunoenzyme assay.Results: the obtained curves of TNF-alpha concentration in the blood were satisfactorily described by the equation for the twocompartment model without absorption. The rapid phase of elimination from the blood took 0–4 h, the slow one — 4–24 h. The highest specific content of protein was observed in the skin, spleen, and kidneys tissue. The calculation of pharmacokinetic parameters demonstrated that the highest values of tissue availability fT were obtained for the kidneys and skin; the drug was retained for longer periods of time in the kidneys, liver and skin (according to the MRT data). As a rule, complete elimination of the drug was observed by the end of the first day after administration.Conclusions: rhTNF-alpha carried by the delivery system was quickly eliminated from the blood and distributed in the internal organ tissues after a single intravenous administration to mice in the effective doses range. The main organs in which rhTNF-alpha was distributed were skin, kidneys, and spleen. The elimination of the drug from the blood was a two-phase process which was generally over by the end of the first day.Основными проблемами использования фактора некроза опухоли альфа (ФНО-альфа) в противоопухолевой терапии являются его быстрая деградация в кровеносном русле и ограниченная селективность накопления в ткани опухоли. В ФБУН ГНЦ ВБ «Вектор» Роспотребнадзора создана биодеградируемая молекулярная конструкция, обеспечивающая защиту от протеаз и адресную доставку белков в ткань опухоли. На основе этой конструкции разработан противоопухолевый препарат, содержащий рекомбинантный ФНО-альфа человека (рчФНО-альфа). Цель работы: изучить фармакокинетику рчФНО-альфа в средстве доставки при его однократном введении. Материалы и методы: препарат рчФНО-альфа в средстве доставки вводили самкам аутбредных мышей ICR (CD-1) однократно внутривенно в двух эффективных противоопухолевых дозах 2,55 мкг и 5,1 мкг на 20 г массы тела. Концентрацию ФНО-альфа в сыворотке крови и супернатантах гомогенатов органов, взятых в разные сроки после введения, определяли иммуноферментным методом. Результаты: полученные кривые изменения содержания в крови ФНО-альфа удовлетворительно описывались уравнением для двухчастевой модели без всасывания. Быстрая фаза процесса выведения из крови приходилась на период 0–4 ч, медленная — 4–24 ч. Наиболее высоким удельное содержание белка было в ткани кожи, селезенке и почках. Расчет фармакокинетических параметров указал на то, что наиболее высокие значения тканевой доступности fT были установлены для почек и кожи; более длительно (в соответствии с данными MRT) препарат удерживался в почках, печени и коже. Процесс элиминации препарата в основном завершался к концу первых суток после введения. Выводы: рчФНО-альфа в средстве доставки при однократном внутривенном введении мышам в диапазоне эффективных доз быстро элиминировался из крови и распределялся по тканям внутренних органов. Основными органами распределения препарата являлись кожа, почки и селезенка. Процесс элиминации препарата из крови носил двухфазный характер и в основном завершался к концу первых суток

Solar Irradiance Forecasting with Natural Language Processing of Cloud Observations and Interpretation of Results with Modified Shapley Additive Explanations

Forecasting the generation of solar power plants (SPPs) requires taking into account meteorological parameters that influence the difference between the solar irradiance at the top of the atmosphere calculated with high accuracy and the solar irradiance at the tilted plane of the solar panel on the Earth’s surface. One of the key factors is cloudiness, which can be presented not only as a percentage of the sky area covered by clouds but also many additional parameters, such as the type of clouds, the distribution of clouds across atmospheric layers, and their height. The use of machine learning algorithms to forecast the generation of solar power plants requires retrospective data over a long period and formalising the features; however, retrospective data with detailed information about cloudiness are normally recorded in the natural language format. This paper proposes an algorithm for processing such records to convert them into a binary feature vector. Experiments conducted on data from a real solar power plant showed that this algorithm increases the accuracy of short-term solar irradiance forecasts by 5–15%, depending on the quality metric used. At the same time, adding features makes the model less transparent to the user, which is a significant drawback from the point of view of explainable artificial intelligence. Therefore, the paper uses an additive explanation algorithm based on the Shapley vector to interpret the model’s output. It is shown that this approach allows the machine learning model to explain why it generates a particular forecast, which will provide a greater level of trust in intelligent information systems in the power industry

Pharmacokinetics of Recombinant Human Tumor Necrosis Factor Alpha in the Delivery System

The main problems of using TNF-alpha in antitumor therapy are its rapid degradation in the bloodstream and the limited selectivity of accumulation in the tumor tissue. The SRC VB «Vector» developed a biodegradable molecular construct that provides protection against proteases and ensures targeted delivery of proteins to the tumor tissue. This construct was used to create an antitumor drug containing recombinant human TNF-alpha (rhTNF-alpha).The aim of the study was to analyse rhTNF-alpha pharmacokinetics in the delivery system after a single administration.Materials and methods: the rhTNF-alpha drug carried by the delivery system was intravenously administered to female outbred ICR (СD-1) mice only once at two effective antitumor doses, 2.55 μg and 5.1 μg / 20 g of body weight. The concentration of TNF-alpha in the serum and supernatants of organ homogenates, obtained at different time points after administration, was analysed by immunoenzyme assay.Results: the obtained curves of TNF-alpha concentration in the blood were satisfactorily described by the equation for the twocompartment model without absorption. The rapid phase of elimination from the blood took 0–4 h, the slow one — 4–24 h. The highest specific content of protein was observed in the skin, spleen, and kidneys tissue. The calculation of pharmacokinetic parameters demonstrated that the highest values of tissue availability fT were obtained for the kidneys and skin; the drug was retained for longer periods of time in the kidneys, liver and skin (according to the MRT data). As a rule, complete elimination of the drug was observed by the end of the first day after administration.Conclusions: rhTNF-alpha carried by the delivery system was quickly eliminated from the blood and distributed in the internal organ tissues after a single intravenous administration to mice in the effective doses range. The main organs in which rhTNF-alpha was distributed were skin, kidneys, and spleen. The elimination of the drug from the blood was a two-phase process which was generally over by the end of the first day

Study on Hemostimulating Properties of Granulocyte-Macrophage Colony Stimulating Factor

The hemostimulating properties of granulocyte-macrophage colony-stimulating factor (GM-CSF) make possible its clinical use in alleviating side effects of anti-cancer radio- and chemotherapy, in bone marrow transplantation, and in the treatment of some primary immunodeficiency conditions associated with leukopenia. The State Research Center of Virology and Biotechnology “Vector” of the Federal Service for Surveillance on Consumer Rights Protection and Human Wellbeing has developed a high-performance technology for production of recombinant human GM-CSF (rhGM-CSF) based on a recombinant E. coli strain. The aim of the study was to assess hemostimulating activity of the rhGM-CSF preparation obtained using the new developed technology, as observed in cell culture and in the mice model of myelosuppression induced by cyclophosphamide administration. Materials and methods: in vitro evaluation of rhGM-CSF hemostimulating activity was performed by MTT assay in the commercial HL-60 promyelocytic leukemia cell culture with preliminary suppression of cell growth rate by adding a low concentration of dimethyl sulfoxide to the medium. In vivo studies were carried out in CBA/CaLac mice with cyclophosphamide-induced myelosuppression. The hemostimulating properties of the drug were evaluated after subcutaneous administration of 1–175 µg/kg doses for 4–5 days, following administration of a cytostatic agent. The total number of leukocytes and the content of their morphological forms were determined in blood samples taken at different time points after the drug administration. The statistical processing of the experimental data was based on analysis of variance using Statgraphics v. 5.0 software. Results: the proliferative activity of HL-60 cells incubated with the rhGM-CSF preparation for 72 hours was shown to be dose-dependent. The highest values of the increase in proliferative activity associated with an increase in the drug dose were observed in the concentration range from 0.04 to 0.64 ng/mL (proliferative activity increased by 11–18% when the dose was increased twofold). The experiments in mice demonstrated a two-phase pattern of the dose-dependent effect. The drug showed the highest hemostimulating effect at the dose of 90 µg/kg. Conclusions: the rhGM-CSF preparation obtained using the new developed technology has a pronounced hemostimulating activity confirmed by both in vitro and in vivo test systems