Short-term forecast methods of electricity generation by solar power plants and its classification

Tyunkov, Dmitry; Gritsay, A. S.; Potapov, V. I.; Хамитов, Р. Н.; Blohin, A. V.; Kondratukova, L. K.

doi:10.1088/1742-6596/1260/5/052033

Cited by 4 publications

(3 citation statements)

References 22 publications

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“…Для гарантирующих поставщиков возникают новые задачи, связанные с построением прогностических моделей и методов краткосрочного прогнозирования потребления электрической энергии для повышения эффективности работы на оптовом рынке электрической энергии и мощности [7]. Такие модели и методы должны учитывать объемы выработки электрической энергии объектами микрогенерации просьюмеров с учетом неопределенностей как режимов работы самих просьюмеров, так и метеорологических факторов [8]. В случае небольшого количества просьюмеров их работа окажет незначительные воздействия на энергосистему.…”

Section: постановка задачиunclassified

On the development of a software and hardware complex for modeling a power grid controller and microgeneration facilities based on renewable energy sources in LV 0,4 kV networks

Gritsay¹,

Chebanenko²,

Potapov³

et al. 2021

Analysis and data processing systems

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The article discusses the issues of connecting microgeneration facilities based on renewable energy sources (RES) to electric networks of low voltage (LV) 0,4 kV. This method of connection will allow the use of microgeneration facilities as network sources of energy, and at the same time unused electrical energy generated by micro-generation facilities can be supplied to a low-voltage network of 0,4 kV. Methods of simulation modeling of the processes of electrical energy distribution in the power supply system with microgeneration objects are proposed; the requirements for the power grid controller used to ensure the connection of micro-generation facilities to the low voltage network are formulated. The problems arising in connection with this are analyzed and the ways of their solution are offered. The proposed methods for the development of software and hardware solutions using modern methods of simulation of electrical energy generation and distribution based on rapid prototyping in the Matlab Simulink environment make it possible to carry out field experiments in order to simulate the processes occurring in 0,4 kV low voltage electrical networks using microgeneration objects prosumers.

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Section: постановка задачиunclassified

On the development of a software and hardware complex for modeling a power grid controller and microgeneration facilities based on renewable energy sources in LV 0,4 kV networks

Gritsay¹,

Chebanenko²,

Potapov³

et al. 2021

Analysis and data processing systems

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“…В настоящее время солнечные электростанции получили широкое распространение во всем мире. Существует ряд методов, которые используются для получения краткосрочного прогноза выработки электроэнергии, применимых к данным источникам, которые можно разделить на 4 класса [1], как представлено на рис.1. Двухэтапный метод прогнозирования электрической энергии солнечными электростанциями, описанный в работе [2], делится на статистический и прогностический этапы.…”

Section: существующие методыunclassified

A neural network model for short-term forecasting of electricity generation by solar power plants

Tyunkov¹,

Gritsay²,

Sapilova³

et al. 2020

Science Bulletin of the NSTU

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Today, energy consumption in the world is growing and it is becoming urgent to solve the problem of replacing traditional energy sources with alternative ones. The solution to this problem is impossible without a preliminary data analysis and further forecasting of energy production by alternative sources. However, the use of alternative energy sources in the conditions of the wholesale electricity and capacity market currently operating on the territory of the Russian Federation is impossible without the use of short-term predictive “day ahead” models. In this article, the authors perform a brief analysis of the existing methods of short-term forecasting which are used when making forecasts for the generation of electricity by solar power plants. Currently, there are already a fairly large number of predictive models built within each of the selected methods of short-term forecasting, and they all differ in their characteristics. Therefore, in order to identify the most promising method of short-term forecasting for further use and development, the authors used a previously developed classification. In the course of the study, a preliminary processing of initial data obtained from the existing solar power plants using spectral analysis was carried out. Further, to build a predictive model, a correlation analysis of the initial data was carried out, which showed the absence of a linear relationship between the components in the retrospective data. Based on the results of the correlation analysis the authors made a decision to select parameters empirically in order to build a predictive model. As a result of the study, a mathematical model based on an artificial neural network was proposed and a learning sample was generated for it. In addition, the architecture of an artificial neural network was determined, the result of which is a short-term forecast of electric power generation in the "day ahead" mode, and calculations were performed to obtain numerical values of the forecast. From the results of the study, it follows that the developed predictive model in the predicted interval has a mean absolute error of about 13.5 MW. However, at some intervals, the peak discrepancies can reach up to 200 MW. The root mean square error of the model is 27.8 MW.

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“…Over the past few years, numerous methods have been proposed to forecast solar radiation values. All existing methods for predicting the generation of electric energy by solar power plants (SPPs) can be categorized into three main groups [20]:…”

Section: Introductionmentioning

confidence: 99%

Short-Term Solar Insolation Forecasting in Isolated Hybrid Power Systems Using Neural Networks

Matrenin,

Manusov,

Nazarov

et al. 2023

Inventions

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Solar energy is an unlimited and sustainable energy source that holds great importance during the global shift towards environmentally friendly energy production. However, integrating solar power into electrical grids is challenging due to significant fluctuations in its generation. This research aims to develop a model for predicting solar radiation levels using a hybrid power system in the Gorno-Badakhshan Autonomous Oblast of Tajikistan. This study determined the optimal hyperparameters of a multilayer perceptron neural network to enhance the accuracy of solar radiation forecasting. These hyperparameters included the number of neurons, learning algorithm, learning rate, and activation functions. Since there are numerous combinations of hyperparameters, the neural network training process needed to be repeated multiple times. Therefore, a control algorithm of the learning process was proposed to identify stagnation or the emergence of erroneous correlations during model training. The results reveal that different seasons require different hyperparameter values, emphasizing the need for the meticulous tuning of machine learning models and the creation of multiple models for varying conditions. The absolute percentage error of the achieved mean for one-hour-ahead forecasting ranges from 0.6% to 1.7%, indicating a high accuracy compared to the current state-of-the-art practices in this field. The error for one-day-ahead forecasting is between 2.6% and 7.2%.

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Short-term forecast methods of electricity generation by solar power plants and its classification

Cited by 4 publications

References 22 publications

On the development of a software and hardware complex for modeling a power grid controller and microgeneration facilities based on renewable energy sources in LV 0,4 kV networks

On the development of a software and hardware complex for modeling a power grid controller and microgeneration facilities based on renewable energy sources in LV 0,4 kV networks

A neural network model for short-term forecasting of electricity generation by solar power plants

Short-Term Solar Insolation Forecasting in Isolated Hybrid Power Systems Using Neural Networks

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