Forecasting of Turkey's electricity generation and CO<sub>2</sub> emissions in estimating capacity factor

Şahin, Utkucan

doi:10.1002/ep.13076

Cited by 16 publications

(4 citation statements)

References 40 publications

Supporting

Mentioning

Contrasting

Order By: Relevance

“…Furthermore, F ‐test assessment is performed to analyze the significance between the actual data and the models. Although results indicate that quartic and hexic models have p value more than .05, the hexic model is best fitted, indicating that there is no significant difference between the hexic model and real data (Figure ) …”

Section: Resultsmentioning

confidence: 96%

Assessment of 50 kWp rooftop solar photovoltaic plant at The ICFAI University, Jaipur: A case study

Mukherji

Mathur

Bhati

et al. 2019

Env Prog and Sustain Energy

View full text Add to dashboard Cite

Currently, India is espousing nonconventional energy sources at an express rate owing to concerns about climate change, emission of harmful greenhouse gases (GHG), and exhausting conventional energy sources. The solar photovoltaic (SPV) plants are, hence, expected to play a noteworthy role to meet energy security and sustainability goals. Contemporary studies reveal substantial ecological concerns associated with installing the ground mounted SPV plants in urban locations. Further observations depict that aforementioned plants need additional land, transmission, and distribution infrastructures. Consequently, rooftop SPV plants are the best solution to produce energy in urban locations, owing to the availability of a large number of empty rooftop spaces with least capacity expenditure. The present article discusses the techno-economic and ecological aspects of a 50 kWp rooftop SPV plant installed at ICFAI University, Jaipur. The plant has produced around 64.149 MWh in the year 2017. The technical assessment focuses on the effects of the meteorological parameters upon energy generation factors. The annual average final yield, reference yield, capacity utilization factor, and performance ratio found to be 106.9 kWh/kWp/month, 149.7 kWh/kWp/month, 14.64 and 70%, respectively. The economic parameters like net present value, internal rate of return, profitability index, and payback period are computed with 0-50% subsidy rates, which supports the financial viability of the plant. The plant contributed significantly to reducing GHG emissions by mitigating 102 t CO 2 , 128 kg SO 2 , 268 kg NO x , and 7,033 kg ash in the year 2017 as evident from the ecological investigation.Statement of industrial relevance: Manufacturing industries are extremely power intensive, as they utilize a substantial amount of energy during the production process. The utilization of sustainable energy sources can considerably curtail the major overhead cost, that is, power, and at the same time minimize pollution. Solar energy utilization is a viable option as large spaces are available not only on rooftops but also allied spaces like parking lots of the industries for installation of rooftop SPV plants.The industries by virtue of location receive extended periods of unimpeded sunlight, as these setups are positioned in separate industrial areas away from residential

show abstract

Section: Resultsmentioning

confidence: 96%

Assessment of 50 kWp rooftop solar photovoltaic plant at The ICFAI University, Jaipur: A case study

Mukherji

Mathur

Bhati

et al. 2019

Env Prog and Sustain Energy

View full text Add to dashboard Cite

show abstract

“…In this study, the energy amounts, which were obtained from the BP, generated by the renewable sources of solar, wind, geothermal and biomass and hydraulic power plants between 1965-2019 were used as data. According to a statement made by the EPDK (2020) the share of renewable energy sources (including hydraulic) in total licensed electricity generation was 30.67% in 2018 and42.10% in 2019 (123789 GWh= 123.789 TWh). Total energy amount generated in 2019 was determined to be 134.49 Twh according to the data provided by BP (2020).…”

Section: Conclusion and Policy Implicationsmentioning

confidence: 99%

Yapay Sinir Ağları ve ARIMA Modeli ile Türkiye Için Yenilenebilir Enerji Üretiminin Tahmini: 2023 Yenilenebilir Enerji Kaynaklarına Göre Üretim Hedefleri

Albayrak

2023

Verimlilik Dergisi

View full text Add to dashboard Cite

Purpose: Turkey attaches particular importance to the energy production with renewable energy sources in order to overcome the negative economic, environmental and social effects which are caused by fossil resources in energy production. Renewable energy sources (RES) do not have any negative effects that threaten the sustainable economic development, such as foreign dependency on the energy and greenhouse gases, such as fossil sources, and renewable energy sources are domestic sources. In this respect, estimating the amount of energy to be produced with RES is one of the important issues for Turkey. The aim of this study is to propose a model for forecasting the amount of energy to be produced for Turkey using renewable energy resources. Methdology: In this study, a forecasting model was created by using the generatio amount of energy generation from renewable sources data between 1965 and 2019 and by using Artificial Neural Networks (ANN) and Autoregressive Integrated Moving Average (ARIMA) methods. Findings While it was estimated that 127.516 TWh of energy will be produced in 2023 with the ANN method, this amount was estimated as 45,457 TeraWatt Hours (TWh) with the ARIMA (1,1,6) model. Mean Absolute Percent Error (MAPE) was calculated in order to determine the margin of error of the forecasting models. These values were determined as 13.1% for the ANN model and 21.9% for the ARIMA model. These results show that the ANN model gives a more appropriate estimation result. Originality: In this research, a new model was proposed for the amount of energy to be obtained from RES in Turkey. It is thought that the results obtained in this study will be useful in energy planning and management.

show abstract

“…To accomplish this task, exponentially damped sinusoidal (EDS) et al are used, and the influencing factors of the input include the capacity factor of thermal, hydro, geothermal, wind, and solar power plants from 2006 to 2016. Results show that EDS is the best suitable for EP 28 . The above statistical prediction models have their advantages and disadvantages, and their scope of application is also different.…”

Section: Introductionmentioning

confidence: 99%

Fastest‐growing source prediction of US electricity production based on a novel hybrid model using wavelet transform

Qiao¹,

Li²,

Liu

et al. 2021

Intl J of Energy Research

View full text Add to dashboard Cite

Electricity is an important indicator for economic development, especially electricity production (EP), which is electricity industry managers making strategic decisions. There are many ways to produce electricity, which is the source of rapid growth in EP is rarely studied. Due to the nonstationary and nonlinearity of the EP time series, traditional methods are less robust to predict it. In this study, a novel combination prediction model is proposed based on wavelet transform (WT), long short-term memory (LSTM), and stacked autoencoder (SAE). Comparisons between the SAE-LSTM and the advanced prediction model. We compared SAE-LSTM and the advanced prediction model including BP (Back Propagation) etc. In addition, the performance comparison of the different wavelet layers based on SAE-LSTM and the performance comparison of the EMD and EEMD based on SAE-LSTM are also compared. At last, future average growth rates (June 2021 ! December 2022) are predicted. The empirical result shows that the combination model in view of SAE-LSTM exceeds the benchmark models. The results also imply that WT-SAE-LSTM outperforms the EMD, EEMD-SAE-LSTM, and SAE. Based on the optimal orders and layers of Coiflets combining with SAE-LSTM, natural gas is the fastest-growing source of EP in the United States.

show abstract

Forecasting of Turkey's electricity generation and CO₂ emissions in estimating capacity factor

Cited by 16 publications

References 40 publications

Assessment of 50 kWp rooftop solar photovoltaic plant at The ICFAI University, Jaipur: A case study

Assessment of 50 kWp rooftop solar photovoltaic plant at The ICFAI University, Jaipur: A case study

Yapay Sinir Ağları ve ARIMA Modeli ile Türkiye Için Yenilenebilir Enerji Üretiminin Tahmini: 2023 Yenilenebilir Enerji Kaynaklarına Göre Üretim Hedefleri

Fastest‐growing source prediction of US electricity production based on a novel hybrid model using wavelet transform

Contact Info

Product

Resources

About

Forecasting of Turkey's electricity generation and CO2 emissions in estimating capacity factor

Cited by 16 publications

References 40 publications

Assessment of 50 kWp rooftop solar photovoltaic plant at The ICFAI University, Jaipur: A case study

Assessment of 50 kWp rooftop solar photovoltaic plant at The ICFAI University, Jaipur: A case study

Yapay Sinir Ağları ve ARIMA Modeli ile Türkiye Için Yenilenebilir Enerji Üretiminin Tahmini: 2023 Yenilenebilir Enerji Kaynaklarına Göre Üretim Hedefleri

Fastest‐growing source prediction of US electricity production based on a novel hybrid model using wavelet transform

Contact Info

Product

Resources

About

Forecasting of Turkey's electricity generation and CO₂ emissions in estimating capacity factor