Assessment of Electricity Generation Using Deep Learning on Solar Power Plants

Yunus Emre Kıymaz; Hidayet Oguz

Article Details

Assessment of Electricity Generation Using Deep Learning on Solar Power Plants

Authors:

Yunus Emre Kıymaz | Hidayet Oguz

Publisher:

Necmettin Erbakan University

Main Topics:

Derin Öğrenme | Güneş Enerjisi Sistemleri

Journal:

Necmettin Erbakan Üniversitesi Fen ve Mühendislik Bilimleri Dergisi Vol.6 Issue 2, pp.289-311

Document Type:

Article

Publication Date:

8/2024

Document Language:

English

Document ID (Org No.):

2001323449

pISSN:

2667-7989

Keywords:

Abstract:

 Solar panel technology is expensive to install such systems, which have a lifespan of about 25 years on average. It is often important to make production estimates for the future to make optimal use of these systems. This study assesses three two-year daily frequency data sets and a one-year hourly frequency data set from the solar power plants univariate time series based in Konya, which have a 1MW capacity per annum. Electricity production analysis is conducted based on the data from the solar power plants using deep learning. The preferred method is determined to be Long Short-Term Memory LSTM, and it has been compared with another statistical method used in time series analysis, Seasonal Autoregressive Integrated Moving Average SARIMA. The results obtained with each dataset have been subjected to five different performance measurement mechanisms MSE, RMSE, NMSE, MAE, MAPE and R2. It has been observed that the LSTM model generally provides results closer to real data compared to the SARIMA model. According to the RMSE score, the average value of four power plants is 973 in LSTM and 1361 in SARIMA, in this case LSTM gave a successful result compared to SARIMA. Before establishing a solar power plant, carrying out a feasibility study has a profitability-enhancing role.

 Solar panel technology is expensive to install such systems, which have a lifespan of about 25 years on average. It is often important to make production estimates for the future to make optimal use of these systems. This study assesses three two-year daily frequency data sets and a one-year hourly frequency data set from the solar power plants univariate time series based in Konya, which have a 1MW capacity per annum. Electricity production analysis is conducted based on the data from the solar power plants using deep learning. The preferred method is determined to be Long Short-Term Memory LSTM, and it has been compared with another statistical method used in time series analysis, Seasonal Autoregressive Integrated Moving Average SARIMA. The results obtained with each dataset have been subjected to five different performance measurement mechanisms MSE, RMSE, NMSE, MAE, MAPE and R2. It has been observed that the LSTM model generally provides results closer to real data compared to the SARIMA model. According to the RMSE score, the average value of four power plants is 973 in LSTM and 1361 in SARIMA, in this case LSTM gave a successful result compared to SARIMA. Before establishing a solar power plant, carrying out a feasibility study has a profitability-enhancing role.

 Solar panel technology is expensive to install such systems, which have a lifespan of about 25 years on average. It is often important to make production estimates for the future to make optimal use of these systems. This study assesses three two-year daily frequency data sets and a one-year hourly frequency data set from the solar power plants univariate time series based in Konya, which have a 1MW capacity per annum. Electricity production analysis is conducted based on the data from the solar power plants using deep learning. The preferred method is determined to be Long Short-Term Memory LSTM, and it has been compared with another statistical method used in time series analysis, Seasonal Autoregressive Integrated Moving Average SARIMA. The results obtained with each dataset have been subjected to five different performance measurement mechanisms MSE, RMSE, NMSE, MAE, MAPE and R2. It has been observed that the LSTM model generally provides results closer to real data compared to the SARIMA model. According to the RMSE score, the average value of four power plants is 973 in LSTM and 1361 in SARIMA, in this case LSTM gave a successful result compared to SARIMA. Before establishing a solar power plant, carrying out a feasibility study has a profitability-enhancing role.

 Güneş paneli teknolojisi ortalama 25 yıl ömrü olan bu tür sistemlerin kurulumu pahalıdır. Bu sistemlerden en iyi şekilde yararlanmak için geleceğe yönelik üretim tahminleri yapmak çoğu zaman önemlidir. Bu çalışmada, Konya merkezli yıllık 1MW kapasiteye sahip güneş enerjisi santrallerine tek değişkenli zaman serisi ait iki yıllık üç günlük frekans veri seti ve bir yıllık saatlik frekans veri seti değerlendirilmektedir. Elektrik üretim analizi, derin öğrenme kullanılarak güneş enerjisi santrallerinden elde edilen verilere dayanılarak yapılmaktadır. Tercih edilen yöntem uzun kısa süreli hafıza LSTM olup, zaman serisi analizinde kullanılan diğer bir istatistiksel yöntem olan mevsimsel otoregresif bütünleşik hareketli ortalama SARIMA ile kıyaslanmıştır. Her bir veri seti ile elde edilmiş sonuçlar beş farklı performans ölçüm mekanizmasına MSE, RMSE, NMSE, MAE, MAPE ve R2 tabi tutulmuş ve LSTM modelinin genellikle SARIMA modeline göre daha gerçek verilere yakın sonuçlar verdiği tespit edilmiştir. RMSE skoruna göre dört santralin ortalama değeri LSTMde 973, SARIMAda 1361 olup, bu durumda LSTM, SARIMAya göre başarılı bir sonuç vermiştir. Güneş enerjisi santrali kurmadan önce fizibilite çalışmasının yapılması karlılığı artırıcı bir role sahiptir.

Article Details

Assessment of Electricity Generation Using Deep Learning on Solar Power Plants

Authors:

Yunus Emre Kıymaz | Hidayet Oguz

Publisher:

Necmettin Erbakan University

Main Topics:

Derin Öğrenme | Güneş Enerjisi Sistemleri

Journal:

Necmettin Erbakan Üniversitesi Fen ve Mühendislik Bilimleri Dergisi Vol.6 Issue 2, pp.289-311

Document Type:

Article

Publication Date:

8/2024

Document Language:

English

Document ID (Org No.):

2001323449

pISSN:

2667-7989

Deep Learning

Long Short-Term Memory

Seasonal Autoregressive Integrated Moving Average

Solar Power Plant

Univariate Time SeriesDerin Öğrenme

Güneş Enerjisi Santrali

Mevsimsel Otoregresif Bütünleşik Hareketli Ortalama

Uzun Kısa Süreli Hafıza

Tek Değişkenli Zaman Serileri

Issues

Article Details

Authors: Yunus Emre Kıymaz | Hidayet Oguz

Publisher: Necmettin Erbakan University

Main Topics: Derin Öğrenme | Güneş Enerjisi Sistemleri

Journal: Necmettin Erbakan Üniversitesi Fen ve Mühendislik Bilimleri Dergisi Vol.6 Issue 2, pp.289-311

Document Type: Article

Publication Date: 8/2024

Document Language: English

Document ID (Org No.): 2001323449

pISSN: 2667-7989

Issues

Authors:

Yunus Emre Kıymaz | Hidayet Oguz

Publisher:

Necmettin Erbakan University

Main Topics:

Derin Öğrenme | Güneş Enerjisi Sistemleri

Journal:

Necmettin Erbakan Üniversitesi Fen ve Mühendislik Bilimleri Dergisi Vol.6 Issue 2, pp.289-311

Document Type:

Article

Publication Date:

8/2024

Document Language:

English

Document ID (Org No.):

2001323449

pISSN:

2667-7989