Prediction of 10-min, hourly, and daily atmospheric air temperature: comparison of LSTM, ANFIS-FCM, and ARMA

Özbek, Arif; Şekertekin, Aliihsan; Bilgili, Mehmet; Arslan, Niyazi

doi:10.1007/s12517-021-06982-y

Cited by 27 publications

(8 citation statements)

References 78 publications

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“…Nowcasting is an ultra-short-range weather forecast with a 5-15-minute time step. This forecast is reduced to the task of extrapolating observed meteorological phenomena (Ozbek et al, 2021) (6):…”

Section: Mare % Interpretationsmentioning

confidence: 99%

Efficient electricity generation forecasting from solar power plants using technology: Integration, benefits and prospects

Stoliarov

2024

bcstu

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Accurate prediction of electricity generation from renewable sources is an essential element to ensure the stability of electricity systems and the transition to more sustainable energy production. The study aims to optimise the operation of Ukrainian power systems through the introduction of the required share of renewable energy sources to ensure the reliability of the power system. To study the accuracy of forecasting electricity generation by photovoltaic power plants in Ukraine, data analysis, a review of existing forecasting models and methods, and comparative analysis using satellite images and meteorological observations were used. Low accuracy of forecasting output is a feature of electricity generation from renewable energy sources, which is explained by the random nature of energy sources and related meteorological conditions. In Ukraine, the problem of qualitative forecasting of electricity generation from renewable sources is becoming more relevant. The importance of finding effective methods for forecasting electricity generation in Ukraine has increased with the emergence of the electricity market. This study addresses the issue of forecasting electricity generation by photovoltaic power plants for the day ahead in the conditions of the Ukrainian energy market. As part of the study, the issues of Ukrainian legislation regarding the requirements for the accuracy of electricity generation forecasting and the consequences of their failure were considered. The study also reviewed modern models and methods for forecasting electricity generation by photovoltaic power plants and explored the new “forecasting system market” in Ukraine. The study presents accepted forecasting metrics that allow estimating errors and comparing the effectiveness of different forecasting methods. Considering the dependence of electricity generation forecasting on meteorological parameters, a comparative analysis of forecasting accuracy using satellite images and meteorological observations was carried out. The study will determine the material presented in determining the model for forecasting electricity generation, thus increasing the efficiency of energy companies in the conditions of the Ukrainian energy market. The study will also reduce the negative impact of the energy sector on the environment and contribute to a more efficient and stable electricity system in the future

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Section: Mare % Interpretationsmentioning

confidence: 99%

Efficient electricity generation forecasting from solar power plants using technology: Integration, benefits and prospects

Stoliarov

2024

bcstu

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“…Ozbek et al [29] evaluated different time interval temperature variations for atmospheric air by using a recurrent neural network. The authors in [30] used a LSTM network to predict the indoor air temperature by using IoT data.…”

Section: Literature Surveymentioning

confidence: 99%

Big Data Analytics Using Swarm-Based Long Short-Term Memory for Temperature Forecasting

Patil¹,

Rekha²,

Solanki³

et al. 2022

Computers, Materials &Amp; Continua

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In the past few decades, climatic changes led by environmental pollution, the emittance of greenhouse gases, and the emergence of brown energy utilization have led to global warming. Global warming increases the Earth's temperature, thereby causing severe effects on human and environmental conditions and threatening the livelihoods of millions of people. Global warming issues are the increase in global temperatures that lead to heat strokes and high-temperature-related diseases during the summer, causing the untimely death of thousands of people. To forecast weather conditions, researchers have utilized machine learning algorithms, such as autoregressive integrated moving average, ensemble learning, and long short-term memory network. These techniques have been widely used for the prediction of temperature. In this paper, we present a swarm-based approach called Cauchy particle swarm optimization (CPSO) to find the hyperparameters of the long shortterm memory (LSTM) network. The hyperparameters were determined by minimizing the LSTM validation mean square error rate. The optimized hyperparameters of the LSTM were used to forecast the temperature of Chennai City. The proposed CPSO-LSTM model was tested on the openly available 25-year Chennai temperature dataset. The experimental evaluation on MAT-LABR2020a analyzed the root mean square error rate and mean absolute error to evaluate the forecasted output. The proposed CPSO-LSTM outperforms the traditional LSTM algorithm by reducing its computational time to 25 min under 200 epochs and 150 hidden neurons during training. The proposed hyperparameter-based LSTM can predict the temperature accurately by having a root mean square error (RMSE) value of 0.250 compared with the traditional LSTM of 0.35 RMSE.

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“…An LSTM layer gets to know long-term dependencies between time steps of sequence data. Next, the network completes the function with regression and fully connected output layers (Bilgili et al, 2021;Ozbek et al, 2021;Sekertekin et al, 2021).…”

Section: Long-short Term Memory (Lstm) Neural Networkmentioning

confidence: 99%

Daily average relative humidity forecasting with LSTM neural network and ANFIS approaches

Özbek

Ünal

Bilgili

2022

Theor Appl Climatol

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Because hurricanes, droughts, oods, and heat waves are all important factors in measuring environmental changes, they can all result from changes in atmospheric air temperature and relative humidity (RH). Besides, climate, weather, industry, human health, and plant growth are all affected by RH.Accurately and consistently forecasting RH is a challenge due to its non-linear nature. The present study tried to predict one day ahead of RH in determined provinces from different climatic regions of Turkey (Ankara, Erzurum, Samsun, Diyarbakır, Antalya, and Bilecik) using long short-term memory (LSTM) and adaptive neuro-fuzzy inference system (ANFIS) with fuzzy c-means (FCM) based machine learning models. As evaluation criteria, root mean square error (RMSE), mean absolute error (MAE), and correlation coe cient (R) were employed. The outcomes from the forecasting models were also validated using observed data. During the testing stage, the smallest MAE and RMSE values were discovered to be 5.76% and 7.51%, respectively, in Erzurum province, with an R-value of 0.892 when using the LSTM method.Moreover, the smallest MAE and RMSE values were obtained to be 5.95% and 7.67% respectively, in Erzurum province with an R-value of 0.887 using the ANFIS model according to the hourly RH prediction.The results indicate that both the LSTM and ANFIS approaches performed well in daily RH prediction, with the LSTM and ANFIS approaches producing nearly identical results.

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Prediction of 10-min, hourly, and daily atmospheric air temperature: comparison of LSTM, ANFIS-FCM, and ARMA

Cited by 27 publications

References 78 publications

Efficient electricity generation forecasting from solar power plants using technology: Integration, benefits and prospects

Efficient electricity generation forecasting from solar power plants using technology: Integration, benefits and prospects

Big Data Analytics Using Swarm-Based Long Short-Term Memory for Temperature Forecasting

Daily average relative humidity forecasting with LSTM neural network and ANFIS approaches

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