Overview of Electrical Energy Forecasting Methods and Models in Renewable Energy

Jurj, Dacian I.; Micu, Dan D.; Muresan, Andrei

doi:10.1109/icepe.2018.8559807

Cited by 19 publications

(7 citation statements)

References 19 publications

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“…The need for interconnectivity and balance between production and consumption of electrical energy is an “impetuous agreement” of clean data [ 49 ]. Any forecasting process [ 66 , 67 ], or statistical model can be jeopardized by the absence of a cleaned data set.…”

Section: Results and Observationsmentioning

confidence: 99%

Custom Outlier Detection for Electrical Energy Consumption Data Applied in Case of Demand Response in Block of Buildings

Jurj

Czumbil

Bargauan

et al. 2021

Sensors

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The aim of this paper is to provide an extended analysis of the outlier detection, using probabilistic and AI techniques, applied in a demo pilot demand response in blocks of buildings project, based on real experiments and energy data collection with detected anomalies. A numerical algorithm was created to differentiate between natural energy peaks and outliers, so as to first apply a data cleaning. Then, a calculation of the impact in the energy baseline for the demand response computation was implemented, with improved precision, as related to other referenced methods and to the original data processing. For the demo pilot project implemented in the Technical University of Cluj-Napoca block of buildings, without the energy baseline data cleaning, in some cases it was impossible to compute the established key performance indicators (peak power reduction, energy savings, cost savings, CO2 emissions reduction) or the resulted values were far much higher (>50%) and not realistic. Therefore, in real case business models, it is crucial to use outlier’s removal. In the past years, both companies and academic communities pulled their efforts in generating input that consist in new abstractions, interfaces, approaches for scalability, and crowdsourcing techniques. Quantitative and qualitative methods were created with the scope of error reduction and were covered in multiple surveys and overviews to cope with outlier detection.

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Section: Results and Observationsmentioning

confidence: 99%

Custom Outlier Detection for Electrical Energy Consumption Data Applied in Case of Demand Response in Block of Buildings

Jurj

Czumbil

Bargauan

et al. 2021

Sensors

Self Cite

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show abstract

“…Moreover, the comparative analysis revealed that while the hybrid CNN-LSTM model demonstrated promising results, it did not significantly outperform the standalone LSTM model in this specific context. Although LSTM and CNN models offer many advantages and are powerful in predicting renewable energy production, especially in the short term [42], they present some limitations inherent to their architecture and operational frameworks. The main disadvantage of LSTM models is their complexity and computational intensity [43,44].…”

Section: Discussionmentioning

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

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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.

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“…Current approaches for solar energy prediction focus on a range of supervised and unsupervised learning techniques such as Support Vector Machines (SVM), decision trees, k-nearest neighbors, or Gaussian processes [43]. Methods such as Artificial Neural Networks (ANN) and SVM, which are statistical methods, provide more reliable solutions for predicting global and horizontal solar radiation and power generation [44]. These methods have been previously used for solar radiation prediction and achieved satisfactory performance.…”

Section: Methodsmentioning

confidence: 99%

Fatigue Analysis of Leaf Springs

Erkman

2023

Interdisciplinary Studies on Contemporary Research Practices in Engineering in the 21st Century-Iii

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This study aims to design leaf springs, also known as scissor springs, with the help of SolidWorks 2018 program in computer environment and then analyze them with the help of Ansys Workbench 18.1 program in computer environment. In this study, we designed the parabolic leaf spring sample, which was actually produced (Type - 1), in a computer environment and then made the necessary examinations by performing finite element analysis. In addition, in order to see the difference more clearly, the analysis was made with the modified version of the sample (Type - 2). The investigations are also written in sub-headings. The main purpose of this study is to design a leaf spring in a short period of time and to start mass production of leaf springs without the need for physical tests.

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Overview of Electrical Energy Forecasting Methods and Models in Renewable Energy

Cited by 19 publications

References 19 publications

Custom Outlier Detection for Electrical Energy Consumption Data Applied in Case of Demand Response in Block of Buildings

Custom Outlier Detection for Electrical Energy Consumption Data Applied in Case of Demand Response in Block of Buildings

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

Fatigue Analysis of Leaf Springs

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