IMPLEMENTATION OF 40 MW PHOTOVOLTAIC POWER PLANT BITOLA IN THE REPUBLIC OF NORTH MACEDONIA

TITLE
IMPLEMENTATION OF 40 MW PHOTOVOLTAIC POWER PLANT BITOLA IN THE REPUBLIC OF NORTH MACEDONIA

AUTHOR(S)
Elizabeta Arsova, Plamen Tsankov

ABSTRACT
From 2021 until today, RN Macedonia is facing a significant shortage of electricity for consumers. According to the energy balances, the import of electricity is increasing, while the domestic production of electricity is decreasing. The reason for this situation, which also contributed to the emergence of an energy crisis, is that the largest domestic electricity producer JSC ESM Macedonia produces smaller and smaller amounts of electricity. The largest producers of electrical energy, TPP Bitola and TPP Oslomej are 50-60 years old, the coal from the Suvodol and Oslomej mines is nearing its end and has a very low calorific value. According to the EU directives, it is necessary for thermal power plants, which represent the biggest polluters of electricity, to gradually shut down and to use renewable sources of electricity as much as possible. Each country, depending on its location, geographical location, as well as climatic conditions, should make significant use of its renewable energy resources. Macedonia is blessed with many sunny days, and the solar radiation ranges from 1 168 kWh/m 2 to 1 650 kWh/m 2, but unfortunately the use of the sun's energy is the least used.
In this working paper, the power for connecting a photovoltaic power plant PvPP Bitola with an installed ower of 40 MW to the power grid will be considered. PvPP Bitola with 40 MW is located in the south-western region of RN Macedonia, where solar radiation is significantly high and amounts to 1 544,9 kWh/m 2. Several variants will be shown for choosing the equipment that would produce the largest amount of electricity. The equipment with the lowest price, as well as its coverage in the selected location, is taken as the most favourable variant. For the selection of the location, land owned by JSC ESM Macedonia was taken, and thus there will be no additional costs for the land, and it is also close to the existing TS 400/100 kV/kV, so the total investment would cost less.

DOI

 www.doi.org/10.70456/WBRM3599

PAGES

 28-33

 

DOWNLOAD

 https://unitechsp.tugab.bg/images/2023/1-EE/s1_p44_v3.pdf

 

How to cite this article:
Elizabeta Arsova, Plamen Tsankov, IMPLEMENTATION OF 40 MW PHOTOVOLTAIC POWER PLANT BITOLA IN THE REPUBLIC OF NORTH MACEDONIA, UNITECH – SELECTED PAPERS - 2024, 28-33

 

PREDICTING WIND ENERGY PRODUCTION IN THE SHORT-TERM USING MACHINE LEARNING ALGORITHM

TITLE
PREDICTING WIND ENERGY PRODUCTION IN THE SHORT-TERM USING MACHINE LEARNING ALGORITHM

AUTHOR(S)
Hilmi KUŞÇU, Taşkın TEZ

ABSTRACT
The prediction of electricity generation from wind power plays a critical role in the formulation and management of future energy production plans. These predictions are highly important for wind energy facilities to achieve optimal performance, meet energy demands, and stabilize energy prices. Therefore, in this study, the Support Vector Machine (SVM) Regression Algorithm, a traditional machine learning algorithm, was preferred to forecast the weekly electricity production of wind power plants. Performance metrics such as Mean Squared Error (MSE), Root Mean Squared Error (RMSE), Mean Absolute Error (MAE), Mean Absolute Percentage Error (MAPE), and R-Squared (R²) were utilized to assess the accuracy of the predictions. The results of this study indicated that the SVM Regression Algorithm with the Radial function yielded the best outcomes. Consequently, it is recommended to employ the SVM Regression Algorithm with the Radial function for weekly electricity production predictions.

DOI
www.doi.org/10.70456/QNJJ9806

PAGES
23-27

DOWNLOAD
https://unitechsp.tugab.bg/images/2023/1-EE/s1_p9_v6.pdf

How to cite this article:
Hilmi KUŞÇU, Taşkın TEZ, PREDICTING WIND ENERGY PRODUCTION IN THE SHORT-TERM USING MACHINE LEARNING ALGORITHM, UNITECH – SELECTED PAPERS - 2024, 23-27