Long Short Term Memory and Rolling Window Technique for Modeling Power Demand Prediction

Aparna, S.

doi:10.1109/iccons.2018.8663115

Cited by 9 publications

(2 citation statements)

References 7 publications

Supporting

Mentioning

Contrasting

Order By: Relevance

“…TensorFlow and Keras as deep learning frameworks were used to implement LSTM networks. As a result of the experiment, they argued that it could be a different solution to smart grids by automating the process with the Internet of Things (IoT) and that the model would perform better with the size of the dataset [20].…”

Section: Literature Reviewmentioning

confidence: 99%

A Comparative Study of AI Methods on Renewable Energy Prediction for Smart Grids: Case of Turkey

Unsal,

Aksoz,

Oyucu

et al. 2024

Sustainability

View full text Add to dashboard Cite

Fossil fuels still have emerged as the predominant energy source for power generation on a global scale. In recent years, Turkey has experienced a notable decrease in the production of coal and natural gas energy, juxtaposed with a significant rise in the production of renewable energy sources. The study employed neural networks, ANNs (artificial neural networks), and LSTM (long short-term memory), as well as CNN (convolutional neural network) and hybrid CNN-LSTM designs, to assess Turkey’s energy potential. Real-time outcomes were produced by integrating these models with meteorological data. The objective was to design strategies for enhancing performance by comparing various models of outcomes. The data collected for Turkey as a whole are based on average values. Machine learning approaches were employed to mitigate the error rate seen in the acquired outcomes. Comparisons were conducted across light gradient boosting machine (LightGBM), gradient boosting regressor (GBR), and random forest regressor (RF) techniques, which represent machine learning models, alongside deep learning models. Based on the findings of the comparative analyses, it was determined that the machine learning model, LightGBM, exhibited the most favorable performance in enhancing the accuracy of predictions. Conversely, the hybrid model, CNN-LSTM, had the greatest rate of inaccuracy. This study will serve as a guide for renewable energy researchers, especially in developing countries such as Turkey that have not switched to a smart grid system.

show abstract

Section: Literature Reviewmentioning

confidence: 99%

A Comparative Study of AI Methods on Renewable Energy Prediction for Smart Grids: Case of Turkey

Unsal,

Aksoz,

Oyucu

et al. 2024

Sustainability

View full text Add to dashboard Cite

show abstract

“…Demand forecasting can reduce inventory, so establishing an accurate demand-forecasting system is a primary objective for enhancing competitiveness. Demand forecasting has been used in various fields, such as electricity demand forecasting [4], tourism demand forecasting [5,6], restaurant demand forecasting [7], oil production forecasting [8], and stock market forecasting [9]. Most of them can initially attain the expected accuracy using statistical approaches or machine learning models, but after some time, they fail to generate the expected answers due to over-reliance on time.…”

Section: Introductionmentioning

confidence: 99%

Hybrid LSTM-ARMA Demand-Forecasting Model Based on Error Compensation for Integrated Circuit Tray Manufacturing

2022

View full text Add to dashboard Cite

Demand forecasting plays a crucial role in a company’s operating costs. Excessive inventory can increase costs and unnecessary waste can be reduced if managers plan for uncertain future demand and determine the most favorable decisions. Managers are demanding increasing accuracy in forecasting as technology advances. Most of the literature discusses forecasting results’ inaccuracy by suspending the model and reloading the data for model retraining and correction, which is extensively employed but causes a bottleneck in practice since users do not have the sufficient ability to correct the model. This study proposes an error compensation mechanism and uses the individuals and moving-range (I-MR) control chart to evaluate the requirement for compensation to solve the current bottleneck using forecasting models. The approach is validated using the case companies’ historical data, and the model is developed using a rolling long short-term memory (LSTM) to output the predicted values; then, five indicators are proposed for screening to determine the prediction statistics to be subsequently employed. Root mean squared error (RMSE), mean absolute percentage error (MAPE), and mean absolute error (MAE) compare the LSTM, rolling LSTM combined index, and LSTM-autoregressive moving average (ARMA) models. The results demonstrate that the RMSE, MAPE, and MAE of LSTM-ARMA are smaller than those of the other two models, indicating that the error compensation mechanism that is proposed in this study can enhance the prediction’s accuracy.

show abstract

Comparative Study of Load Forecasting Techniques in Smart Microgrid

Kurup,

Angel,

Pandi

et al. 2023

Lecture Notes in Electrical Engineering

View full text Add to dashboard Cite

Long Short Term Memory and Rolling Window Technique for Modeling Power Demand Prediction

Cited by 9 publications

References 7 publications

A Comparative Study of AI Methods on Renewable Energy Prediction for Smart Grids: Case of Turkey

A Comparative Study of AI Methods on Renewable Energy Prediction for Smart Grids: Case of Turkey

Hybrid LSTM-ARMA Demand-Forecasting Model Based on Error Compensation for Integrated Circuit Tray Manufacturing

Comparative Study of Load Forecasting Techniques in Smart Microgrid

Contact Info

Product

Resources

About