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 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.

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 A Review on DC Microgrid and Decentralized Approach

2021 ◽  
Vol 9 (8) ◽  
pp. 1719--1723
Author(s):  
Mahesh Abdare
Keyword(s):  
Renewable Energy ◽  
Distribution System ◽  
Renewable Energy Sources ◽  
Energy Generation ◽  
Energy Sources ◽  
Dc Microgrid ◽  
Renewable Energy Generation ◽  
The Given ◽  
Day By Day

Abstract: DC Microgrid is going to be a very important part of the Distribution system soon. The given circumstances have forced us to find how to utilize renewable energy sources in the integration to increase its reliability in our day-to-day life. This paper gives a good idea of the DC Microgrid and various methods being used for the controlling part of it. As day by day cost incurred in renewable energy generation is decreasing, we need to find out significant parts where this kind of DC Microgrid can be utilized to provide electricity in all parts of the country. Keywords: DGUs, ImGs, DMA, OXD, DC Microgrid.

scholarly journals Renewable energy Sector in Gujarat, India

2021 ◽  
Vol 23 (06) ◽  
pp. 1128-1140
Author(s):  
Zahira Tabassum ◽  
◽  
Dr.Chandrashekhar Shastry ◽  
Keyword(s):  
Power Generation ◽  
Renewable Energy ◽  
Power Systems ◽  
Fossil Fuels ◽  
Renewable Energy Sources ◽  
Energy Generation ◽  
Energy Sources ◽  
Energy Integration ◽  
Energy Share ◽  
Renewable Energy Generation

Excessive use of traditional energy sources such as fossil fuels has resulted in significant environmental deterioration. India is one of the world’s fastest-growing energy consumers, and it is making continual efforts to increase renewable energy generation. The use of renewable energy sources to generate electricity is expanding every day. Renewable energy integration with existing power systems is a difficult endeavor that necessitates strategy and development. Climate-friendly energy systems will result from the use of renewable energy sources in power generation, as they lower CO2 emissions caused by fossil fuels used in conventional power generation. This research looks at a renewable energy scenario using Gujarat as a case study, which is a leader in renewable energy generation. The policies taken by the Gujarat government to increase renewable energy’s participation in the energy mix, as well as the challenges and potential solutions for boosting the deployment of renewable energy sources across Gujarat, are discussed. This study can be used as a guide for policymakers and researchers in other states and around the world who want to boost renewable energy share.

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>

scholarly journals DEVELOPMENT OF RENEWABLE ENERGY SOURCES IN THE CONTEXT OF ENERGY MANAGEMENT

2019 ◽  
Vol 6 (3-4) ◽  
pp. 77-87
Author(s):  
VALENTYNA YAKUBIV ◽  
YULIIA MAKSYMIV ◽  
IRYNA HRYHORUK ◽  
NAZARIY POPADYNETS ◽  
IRYNA PIATNYCHUK
Keyword(s):  
Renewable Energy ◽  
Energy Management ◽  
Renewable Energy Sources ◽  
Private Investment ◽  
Energy Generation ◽  
Developed Countries ◽  
Energy Sources ◽  
Investment Risk ◽  
Investment Activity ◽  
Renewable Energy Generation

The paper deals with global trends in energy consumption and renewable energy generation. Worldwide practices in financing of renewable energy production are analysed according to the following dimensions: sources of financing, types of used policy instruments, types of recipients (public or private) and types of financed technologies. The key factors that influence the investment attractiveness of renewable energy sources in the world are presented. Main obstacles impeding the utilisation of potential of renewable energy generation in Ukraine are pointed out from the standpoint of the global development trends, as the experience of economically developed countries are advised to be used for Ukraine. Conditions for investment activity in this field should be created (involving both domestic and foreign investments), stimulating state policy should be implemented, and an energy management based on the international experience should be developed. The problems of renewable energy sources in Ukraine are described, in particular, the presence of investment risk in terms of its components as general economic, legal and financial. In the most developed countries in terms of RES consumption direct public investment is a small proportion of total renewable energy financing, whereas private investment has the major share. A significant obstacle to the possibility of realizing such experience in Ukraine is the presence of investment risk, mainly caused by unstable political conditions (both internal and external). Energy management and monitoring activities of enterprises of various forms of ownership and branch affiliation should be introduced along with the necessity of attracting investments in renewable energy. It is expected that the results presented in this article may be useful for improving the renewable energy development policy both at the country level and at the level of a particular economic entity.

An Efficient Hybrid Forecasting Approach for Wind Speed Time Series

2017 ◽  
Vol 7 (9) ◽  
pp. 13
Author(s):  
Bhargavi Munnaluri ◽  
K. Ganesh Reddy
Keyword(s):  
Neural Networks ◽  
Artificial Neural Networks ◽  
Wind Speed ◽  
Fossil Fuels ◽  
Renewable Energy Sources ◽  
Support Vector ◽  
Hybrid Forecasting ◽  
Artificial Neural ◽  
Future Utilization

Wind forecasting is one of the best efficient ways to deal with the challenges of wind power generation. Due to the depletion of fossil fuels renewable energy sources plays a major role for the generation of power. For future management and for future utilization of power, we need to predict the wind speed.  In this paper, an efficient hybrid forecasting approach with the combination of Support Vector Machine (SVM) and Artificial Neural Networks(ANN) are proposed to improve the quality of prediction of wind speed. Due to the different parameters of wind, it is difficult to find the accurate prediction value of the wind speed. The proposed hybrid model of forecasting is examined by taking the hourly wind speed of past years data by reducing the prediction error with the help of Mean Square Error by 0.019. The result obtained from the Artificial Neural Networks improves the forecasting quality.

scholarly journals Predicting Sooting Propensity of Oxygenated Fuels Using Artificial Neural Networks

Processes ◽  
2021 ◽  
Vol 9 (6) ◽  
pp. 1070
Author(s):  
Abdul Gani Abdul Jameel
Keyword(s):  
Neural Networks ◽  
Artificial Neural Networks ◽  
Functional Groups ◽  
Average Error ◽  
Ann Model ◽  
Oh Groups ◽  
Unseen Data ◽  
Learning Capabilities ◽  
Artificial Neural ◽  
Oxygenated Fuels

The self-learning capabilities of artificial neural networks (ANNs) from large datasets have led to their deployment in the prediction of various physical and chemical phenomena. In the present work, an ANN model was developed to predict the yield sooting index (YSI) of oxygenated fuels using the functional group approach. A total of 265 pure compounds comprising six chemical classes, namely paraffins (n and iso), olefins, naphthenes, aromatics, alcohols, and ethers, were dis-assembled into eight constituent functional groups, namely paraffinic CH3 groups, paraffinic CH2 groups, paraffinic CH groups, olefinic –CH=CH2 groups, naphthenic CH-CH2 groups, aromatic C-CH groups, alcoholic OH groups, and ether O groups. These functional groups, in addition to molecular weight and branching index, were used as inputs to develop the ANN model. A neural network with two hidden layers was used to train the model using the Levenberg–Marquardt (ML) training algorithm. The developed model was tested with 15% of the random unseen data points. A regression coefficient (R2) of 0.99 was obtained when the experimental values were compared with the predicted YSI values from the test set. An average error of 3.4% was obtained, which is less than the experimental uncertainty associated with most reported YSI measurements. The developed model can be used for YSI prediction of hydrocarbon fuels containing alcohol and ether-based oxygenates as additives with a high degree of accuracy.

scholarly journals Modelling and Prediction of Monthly Global Irradiation Using Different Prediction Models

Energies ◽  
2021 ◽  
Vol 14 (8) ◽  
pp. 2332
Author(s):  
Cecilia Martinez-Castillo ◽  
Gonzalo Astray ◽  
Juan Carlos Mejuto
Keyword(s):  
Neural Networks ◽  
Artificial Neural Networks ◽  
Random Forests ◽  
Prediction Models ◽  
Meteorological Station ◽  
Ann Model ◽  
Global Irradiation ◽  
Artificial Neural ◽  
Input Variables ◽  
Using Data

Different prediction models (multiple linear regression, vector support machines, artificial neural networks and random forests) are applied to model the monthly global irradiation (MGI) from different input variables (latitude, longitude and altitude of meteorological station, month, average temperatures, among others) of different areas of Galicia (Spain). The models were trained, validated and queried using data from three stations, and each best model was checked in two independent stations. The results obtained confirmed that the best methodology is the ANN model which presents the lowest RMSE value in the validation and querying phases 1226 kJ/(m2∙day) and 1136 kJ/(m2∙day), respectively, and predict conveniently for independent stations, 2013 kJ/(m2∙day) and 2094 kJ/(m2∙day), respectively. Given the good results obtained, it is convenient to continue with the design of artificial neural networks applied to the analysis of monthly global irradiation.

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