Short-term load analysis and forecasting using stochastic approach considering pandemic effects

Panigrahi, Radharani; Patne, Nita R.; Surya Vardhan, B. V.; Khedkar, Mohan

doi:10.1007/s00202-023-02135-4

Cited by 3 publications

(1 citation statement)

References 22 publications

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“…For example, predictions can guide people, organizations, and governments to choose the best options or strategies to achieve their goals or solve their problems. Another example of the application of predictions can be found in the consumption of electrical energy to guarantee the optimal operating conditions of an energy network that supplies electrical energy to its customers [1][2][3][4][5]. A time series is a set of records about a phenomenon that is ordered equidistantly with respect to time; this is also called a forecast.…”

Section: Introductionmentioning

confidence: 99%

A Literature Review on Some Trends in Artificial Neural Networks for Modeling and Simulation with Time Series

Muñoz-Zavala,

Macías-Díaz,

Alba-Cuéllar

et al. 2024

Algorithms

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This paper reviews the application of artificial neural network (ANN) models to time series prediction tasks. We begin by briefly introducing some basic concepts and terms related to time series analysis, and by outlining some of the most popular ANN architectures considered in the literature for time series forecasting purposes: feedforward neural networks, radial basis function networks, recurrent neural networks, and self-organizing maps. We analyze the strengths and weaknesses of these architectures in the context of time series modeling. We then summarize some recent time series ANN modeling applications found in the literature, focusing mainly on the previously outlined architectures. In our opinion, these summarized techniques constitute a representative sample of the research and development efforts made in this field. We aim to provide the general reader with a good perspective on how ANNs have been employed for time series modeling and forecasting tasks. Finally, we comment on possible new research directions in this area.

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Section: Introductionmentioning

confidence: 99%

A Literature Review on Some Trends in Artificial Neural Networks for Modeling and Simulation with Time Series

Muñoz-Zavala,

Macías-Díaz,

Alba-Cuéllar

et al. 2024

Algorithms

View full text Add to dashboard Cite

show abstract

Optimal peer-to-peer energy trading model with short-term load forecasting for energy market

Manchalwar,

Patne,

Panigrahi

et al. 2024

Electr Eng

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A Feature Engineering-Driven Approach to Detecting Fraud in Ethereum Transactions with Ensemble Models

Chithanuru,

Ramaiah

2024

Preprint

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The Ethereum blockchain, known for its decentralized and open-source nature, has revolutionized online transactions. However, vulnerabilities within Ethereum's architecture have resulted in several security breaches. By analyzing transaction data, malicious activities can be detected, helping to mitigate cyber threats such as phishing, Ponzi schemes, and eclipse, Sybil, and DDoS attacks. Machine learning-based anomaly detection has proven effective across various fields, and this paper introduces a fine-tuned ensemble machine learning model to detect fraudulent transactions on the Ethereum platform. To optimize the identification of fraudulent transactions, XGBoost and Random Forest algorithms are utilized to extract critical features from the transaction dataset. Additionally, data resampling techniques are applied to counteract overfitting. The proposed framework operates in two phases: the first phase assesses the effectiveness of different machine learning models, while the second phase develops an ensemble model based on these findings. The framework's performance is benchmarked against the baseline models and state-of-the-art methods, demonstrating superior results. Testing reveals that the ensemble model achieves an accuracy of 99.4% and a Matthews’s correlation coefficient of 94.9%.

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Short-term load analysis and forecasting using stochastic approach considering pandemic effects

Cited by 3 publications

References 22 publications

A Literature Review on Some Trends in Artificial Neural Networks for Modeling and Simulation with Time Series

A Literature Review on Some Trends in Artificial Neural Networks for Modeling and Simulation with Time Series

Optimal peer-to-peer energy trading model with short-term load forecasting for energy market

A Feature Engineering-Driven Approach to Detecting Fraud in Ethereum Transactions with Ensemble Models

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