scholarly journals Forecasting the Structure of Energy Production from Renewable Energy Sources and Biofuels in Poland

Energies ◽  
2020 ◽  
Vol 13 (10) ◽  
pp. 2539 ◽  
Author(s):  
Jarosław Brodny ◽  
Magdalena Tutak ◽  
Saqib Ahmad Saki
Keyword(s):  
Neural Networks ◽  
Artificial Neural Networks ◽  
Renewable Energy ◽  
Energy Production ◽  
Public Awareness ◽  
Renewable Energy Sources ◽  
Energy Sources ◽  
Production Volume ◽  
The European Union ◽  
Artificial Neural

The world’s economic development depends on access to cheap energy sources. So far, energy has been obtained mainly from conventional sources like coal, gas and oil. Negative climate changes related to the high emissions of the economy based on the combustion of hydrocarbons and the growing public awareness have made it necessary to look for new ecological energy sources. This condition can be met by renewable energy sources. Both social pressure and international activities force changes in the structure of sources from which energy is produced. This also applies to the European Union countries, including Poland. There are no scientific studies in the area of forecasting energy production from renewable energy sources for Poland. Therefore, it is reasonable to investigate this subject since such a forecast can have a significant impact on investment decisions in the energy sector. At the same time, it must be as reliable as possible. That is why a modern method was used for this purpose, which undoubtedly involves artificial neural networks. The following article presents the results of the analysis of energy production from renewable energy sources in Poland and the forecasts for this production until 2025. Artificial neural networks were used to make the forecast. The analysis covered eight main sources from which this energy is produced in Poland. Based on the production volume since 1990, predicted volumes of renewable energy sources until 2025 were determined. These forecasts were prepared for all studied renewable energy sources. Renewable energy production plans and their share in total energy consumption in Poland were also examined and included in climate plans. The research was carried out using artificial neural networks. The results should be an important source of information on the effects of implementing climate policies in Poland. They should also be utilized to develop action plans to achieve the objectives of the European Green Deal strategy.

The Forecasting of Renewable Energy Generation for Turkey by Artificial Neural Networks and a Auto-Regressive Integrated Moving-Average Model -2023 Generation Targets by Renewable Energy Resources

2021 ◽  
Author(s):  
özlem karadag albayrak
Keyword(s):  
Neural Networks ◽  
Artificial Neural Networks ◽  
Renewable Energy ◽  
Moving Average ◽  
Renewable Energy Sources ◽  
Energy Generation ◽  
Energy Sources ◽  
Ann Model ◽  
Artificial Neural ◽  
Renewable Energy Generation

Abstract Turkey attaches particular importance to energy generation by renewable energy sources in order to remove negative economic, environmental and social effects caused by fossil resources in energy generation. Renewable energy sources are domestic and do not have any negative effect, such as external dependence in energy and greenhouse gas, caused by fossil resources and which constitute a threat for sustainable economic development. In this respect, the prediction of energy amount to be generated by Renewable Energy (RES) is highly important for Turkey. In this study, a generation forecasting was carried out by Artificial Neural Networks (ANN) and Autoregressive Integrated Moving Average (ARIMA) methods by utilising the renewable energy generation data between 1965-2019. While it was predicted by ANN that 127.516 TWh energy would be generated in 2023, this amount was estimated to be 45.457 TeraWatt Hour (TWh) by ARIMA (1.1.6) model. The Mean Absolute Percentage Error (MAPE) was calculated in order to specify the error margin of the forecasting models. This value was determined to be 13.1% by ANN model and 21.9% by ARIMA model. These results suggested that the ANN model provided a more accurate result. It is considered that the conclusions achieved in this study will be useful in energy planning and management.

scholarly journals DETERMINATION OF RESS GENERATION USING ARTIFICIAL NEURAL NETWORKS

2020 ◽  
pp. 76-83
Author(s):  
О. Rubanenko ◽  
D. Danylchenko ◽  
V. Teptya
Keyword(s):  
Neural Networks ◽  
Artificial Neural Networks ◽  
Renewable Energy ◽  
Satellite Data ◽  
Electricity Generation ◽  
Renewable Energy Sources ◽  
Energy Sources ◽  
Power Stations ◽  
Photovoltaic Power ◽  
Artificial Neural

Paper considers the perspectives and potential of using renewable energy sources to decide the global warming problem. The World trend of increasing electricity generation by photovoltaic power stations according to the International Renewable Energy Agency and the trend of increasing the installed capacity of photovoltaic power stations in Ukraine, which supply the generated capacity at a "green" tariff according to the National Commission for State Regulation of Energy utilities of Ukraine. Opportunities and conditions of using artificial neural networks to defined the power generation of photovoltaic power stations on the example of the power plant "Tsekinivska-2" 4–5 turns are investigated. A platform developed by the European Commission – Photovoltaic Geographical Information System – was used to create a database for the creation and training of artificial neural networks. Regularities of change of meteorological satellite data and their influence on electricity generation of photovoltaic power stations are established. For this purpose, the software complex MATLAB was used, namely the module for the creation of artificial neural networks – Neural Networks Toolbox. The height of the sun is conditionally considered constant and its value is repeated from year to year or has a slight deviation, so it can be used as an indicator of the hour and can be considered known in advance, so determined by empirical formulas and changes only under certain astrophysical laws. Regarding the temperature at 2 m and the wind at 10 m, these meteorological data are known, as they are needed not only for forecasting the operation of renewable energy sources but also in agriculture. Therefore, data related to solar radiation are considered to be the most problematic, as this value is the most difficult to determine. Satellite data may have an error, the installation of weather stations, namely quality pyranometers is a costly procedure, but will help provide a training sample of quality data. To forecast with satisfactory accuracy, it is necessary to collect data for 1 year of operation of the weather station. The nntool and Anfis MATLAB modules were used to predict generation. But the obtained results can be used to assess the effectiveness of the photovoltaic power stations, but they are unsatisfactory for the operational balancing of the system.

scholarly journals Renewable energy development in Hungary

2012 ◽  
pp. 73-77
Author(s):  
Orsolya Nagy
Keyword(s):  
Renewable Energy ◽  
Energy Production ◽  
Energy Use ◽  
Renewable Energy Sources ◽  
Electricity Production ◽  
Energy Sources ◽  
The European Union ◽  
Fuel Prices ◽  
The Earth ◽  
Renewable Energy Development

Due to the exhaustion of the fossile fuel reserves of the Earth, the increase of fossile fuel prices and the difficulties concerning stable fuel supply, the increase of electricity production from renewable energy sources has a special strategic importance. In this study, I am going to evaluate the circumstances of the production and use of renewable energy sources in Hungary and in the European Union. I present the Hungarian economic, energy policy-related and social circumstances which make it necessary to support renewable energy production. I am going to give an overview on the related EU strategies concerning the sector and the Hungarian development plan in this field. I pay particular attention to the examination of development opportunities and the R&D activities going on in this area in Hungary, as well as the efficiency of the means used to improve renewable energy use.

scholarly journals A Review of the Use of Artificial Neural Network Models for Energy and Reliability Prediction. A Study of the Solar PV, Hydraulic and Wind Energy Sources

2019 ◽  
Vol 9 (9) ◽  
pp. 1844 ◽  
Author(s):  
Jesús Ferrero Bermejo ◽  
Juan F. Gómez Fernández ◽  
Fernando Olivencia Polo ◽  
Adolfo Crespo Márquez
Keyword(s):  
Neural Network ◽  
Artificial Neural Network ◽  
Renewable Energy ◽  
Energy Production ◽  
Renewable Energy Sources ◽  
Operating Conditions ◽  
Energy Sources ◽  
Solar Pv ◽  
Ann Models ◽  
Artificial Neural

The generation of energy from renewable sources is subjected to very dynamic changes in environmental parameters and asset operating conditions. This is a very relevant issue to be considered when developing reliability studies, modeling asset degradation and projecting renewable energy production. To that end, Artificial Neural Network (ANN) models have proven to be a very interesting tool, and there are many relevant and interesting contributions using ANN models, with different purposes, but somehow related to real-time estimation of asset reliability and energy generation. This document provides a precise review of the literature related to the use of ANN when predicting behaviors in energy production for the referred renewable energy sources. Special attention is paid to describe the scope of the different case studies, the specific approaches that were used over time, and the main variables that were considered. Among all contributions, this paper highlights those incorporating intelligence to anticipate reliability problems and to develop ad-hoc advanced maintenance policies. The purpose is to offer the readers an overall picture per energy source, estimating the significance that this tool has achieved over the last years, and identifying the potential of these techniques for future dependability analysis.

ENERGY PRODUCTION AND CONSUMPTION FROM RENEWABLE SOURCES IN POLAND AND 28 COUNTRIES OF THE EUROPEAN UNION

2017 ◽  
Vol XIX (2) ◽  
pp. 263-269
Author(s):  
Paulina Trębska ◽  
Arkadiusz Gromada
Keyword(s):  
European Union ◽  
Renewable Energy ◽  
Energy Production ◽  
Renewable Energy Sources ◽  
Energy Sources ◽  
The European Union ◽  
Renewable Sources ◽  
World Energy ◽  
Production And Consumption ◽  
Structure Of Production

The purpose of this article is to present the changes in the structure of production and consumption of energy from renewable energy sources in Poland and in the European Union. Renewable energy sources account for only about 16% of world energy production. This situation, however, from year to year changes. Prym in the use of energy from renewable sources leads the European Union, which has set itself an ambitious target that by 2020, 20% of the energy extracted from the green renewable energy sources.

scholarly journals Subsidies as Financing Opportunities – the Development of Renewable Energy Sources in Poland

2020 ◽  
Vol 3 (1) ◽  
pp. 407-419
Author(s):  
Ewa W. Maruszewska ◽  
Kęstutis Navickas ◽  
Renata Navickienė
Keyword(s):  
Renewable Energy ◽  
Financial Support ◽  
Energy Production ◽  
Renewable Energy Sources ◽  
Primary Energy ◽  
Energy Sources ◽  
The European Union ◽  
Renewable Energy Resources ◽  
Energy Investment ◽  
Prior Literature

AbstractAs Poland is considered a coal country, renewable energy resources still do not have a significant share in energy production. Further, 14% contribution of renewable energy to total primary energy production in 2020 is endangered. Thus, in order to speed up with renewable energy sources new actions should be stimulated. The aim of the article is to describe the most popular renewable energy installations in Poland and further to search for a case study indicating that an investment in renewable sources is profitable without financial support. The results indicate that prior literature does not present any analysis of profitable renewable energy investment without financial support. It states the need for regulators to implement additional financial support not only on the European Union level but also on the national one.

scholarly journals Comparison of Renewable Energy Sources in ‘New’ EU Member States in the Context of National Energy Transformations

Energies ◽  
2021 ◽  
Vol 14 (23) ◽  
pp. 7963
Author(s):  
Agnieszka Wałachowska ◽  
Aranka Ignasiak-Szulc
Keyword(s):  
Renewable Energy ◽  
Energy Production ◽  
Technological Development ◽  
Renewable Energy Sources ◽  
Energy Sources ◽  
Low Carbon ◽  
The European Union ◽  
Member States ◽  
Eu Member States ◽  
New Eu Member States

The European Union strives to create sustainable, low-carbon economies; therefore, energy policies of all member states should move towards renewable energy sources (RES). That concerns also the so-called new EU member states. These countries, on the one hand, are characterized by significant historical similarities in terms of post-communist legacy and adopted development strategies linked with the EU membership, and on the other hand, by significant social, economic and environmental differences resulting from different transformation and development paths and conditions. The question remains how the selected countries should cope with actions in the field of national energy transformations to confront the multiple challenges linked to assuring a significant level of sustainable development. In order to be successful, it is necessary to conduct an effective and rapid changes in the energy industry, which should be preceded by an analysis of the differentiation of countries in terms of their potentials. The results of such analyses should be helpful in selecting the most appropriate strategies for transformation of the described industry. Therefore, the purpose of the article is to assess the new EU member states for RES diversification and identify similar subgroups of countries using cluster analysis, taking into account the percentage share of individual renewable energy sources in total renewable energy production. This was done for the years 2010, 2015 and 2019 which should allow us to demonstrate the differences between them as a group and also reveal changes recorded over time for a single country. Ward’s method was used for the analysis. The presented approach to the analysis of energy production enabled the acquisition of new knowledge in this field and supported the assessment of the current state of RES. The results obtained can be used in countries of comparable specificity to undertake activities of similar nature in relation to internal energy production, technological development or common energy policy.

scholarly journals Analyzing Similarities between the European Union Countries in Terms of the Structure and Volume of Energy Production from Renewable Energy Sources

Energies ◽  
2020 ◽  
Vol 13 (4) ◽  
pp. 913 ◽  
Author(s):  
Jarosław Brodny ◽  
Magdalena Tutak
Keyword(s):  
European Union ◽  
Renewable Energy ◽  
Energy Production ◽  
Renewable Energy Sources ◽  
Geographical Location ◽  
Energy Transition ◽  
Energy Sources ◽  
The European Union ◽  
Eu Countries ◽  
The Eu

The European Union (EU) countries, as one of the most economically developed regions in the world, are taking increasingly decisive actions to reduce the emission of harmful substances into the natural environment. This can be exemplified by a new climate strategy referred to as “The European Green Deal”. Its basic assumption is that the EU countries will have achieved climate neutrality by 2050. To do so, it is necessary to make an energy transition involving the widest possible use of renewable energy sources (RES) for energy production. However, activities in this area should be preceded by analyses due to the large diversity of the EU countries in terms of economic development, the number of inhabitants and their wealth as well as geographical location and area. The results of such analyses should support the implementation of adopted strategies. In order to assess the current state of the energy sector in the EU and indicate future directions of activities, research was carried out to analyze the structure and volume of energy production from RES in the EU countries. The aim of the study was to divide the EU countries into similar groups by the structure and volume of energy production from RES. This production was compared with the number of inhabitants of each EU country, its area and the value of Gross Domestic Product (GDP). This approach allows a new and broader view of the structure of energy production from RES and creates an opportunity to take into account additional factors when developing and implementing new climate strategies. The k-means algorithm was used for the analysis. The presented analyses and obtained results constitute a new approach to studying the diversified energy market in the EU. The results should be used for the development of a common energy and climate policy and economic integration of the EU countries.

scholarly journals Evaluation of wind energy investment with artificial neural networks

2019 ◽  
Vol 9 (2) ◽  
pp. 142-147
Author(s):  
Hasan Huseyin Yildirim ◽  
Mehmet Yavuz
Keyword(s):  
Neural Network ◽  
Neural Networks ◽  
Artificial Neural Networks ◽  
Renewable Energy ◽  
Wind Speed ◽  
Wind Energy ◽  
Renewable Energy Sources ◽  
Energy Sources ◽  
Energy Investment ◽  
Data Set

Countries aiming for sustainability in economic growth and development ensure the reliability of energy supplies. For countries to provide their energy needs uninterruptedly, it is important for domestic and renewable energy sources to be utilised. For this reason, the supply of reliable and sustainable energy has become an important issue that concerns and occupies mankind. Of the renewable energy sources, wind energy is a clean, reliable and inexhaustible source of energy with low operating costs. Turkey is a rich nation in terms of wind energy potential. Forecasting of investment efficiency is an important issue before and during the investment period in wind energy investment process because of high investment costs. It is aimed to forecast the wind energy products monthly with multilayer neural network approach in this study. For this aim a feed forward back propagation neural network model has been established. As a set of data, wind speed values 48 months (January 2012-December 2015) have been used. The training data set occurs from 36 monthly wind speed values (January 2012-December 2014) and the test data set occurs from other values (January-December 2015). Analysis findings show that the trained Artificial Neural Networks (ANNs) have the ability of accurate prediction for the samples that are not used at training phase. The prediction errors for the wind energy plantation values are ranged between 0.00494-0.015035. Also the overall mean prediction error for this prediction is calculated as 0.004818 (0.48%). In general, we can say that ANNs be able to estimate the aspect of wind energy plant productions.

scholarly journals Integration of artificial neural networks for multi-source energy management in a smart grid

2021 ◽  
Vol 12 (3) ◽  
pp. 1919
Author(s):  
Eziitouni Jarmouni ◽  
Ahmed Mouhsen ◽  
Mohammed Lamhammedi ◽  
Hicham Ouldzira
Keyword(s):  
Neural Networks ◽  
Artificial Neural Networks ◽  
Renewable Energy ◽  
Energy Management ◽  
Management Strategy ◽  
Renewable Energy Sources ◽  
Solar Panels ◽  
Energy Management Strategy ◽  
The Public ◽  
Artificial Neural

<span lang="EN-US">Among the most widespread renewable energy sources is solar energy; Solar panels offer a green, clean, and environmentally friendly source of energy. In the presence of several advantages of the use of photovoltaic systems, the random operation of the photovoltaic generator presents a great challenge, in the presence of a critical load. Among the most used solutions to overcome this problem is the combination of solar panels with generators or with the public grid or both. In this paper, an energy management strategy is proposed with a safety aspect by using artificial neural networks (ANNs), in order to ensure a continuous supply of electricity to consumers with a maximum solicitation of renewable energy.</span>

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