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  1. dugi-doc.udg.edu

    The objective of this paper is to analyze the performance of different forecasting methods for predicting demand at the substation level. Substation level data is the result of aggregating the consumption and generation data of multiple points on the grid.
  2. dugi-doc.udg.edu

    demand) to maintain the grid operating under safe conditions to guarantee the supply. Forecasting demand, generation, and flexibility (capability to change demand, or generation) is essential for the DSO to operate and manage electric power networks [5]. Energy forecast is also necessary to cope with risk management and to prevent potential
  3. ebooks.iospress.nl

    Comparative Analysis of Electricity Demand Forecasting at Substation Level ... The objective of this paper is to analyze the performance of different forecasting methods for predicting demand at the substation level. Substation level data is the result of aggregating the consumption and generation data of multiple points on the grid ...
  4. semanticscholar.org

    DOI: 10.3233/FAIA240442 Corpus ID: 273589504; Comparative Analysis of Electricity Demand Forecasting at Substation Level @inproceedings{Segura2024ComparativeAO, title={Comparative Analysis of Electricity Demand Forecasting at Substation Level}, author={Alex Segura and J{\'e}r{\'e}my Lancereau and Joaqu{\'i}m Mel{\'e}ndez and Carolina M. R. do Carmo}, booktitle={International Conference of the ...
  5. ieeexplore.ieee.org

    Energy demand is exploding worldwide. Accordingly, there is a growing interest in methods for maximizing energy use efficiency. Electricity demand forecasting is a technique of predicting future electricity demand based on past electricity usage data. This makes it possible to effectively manage energy usage. In particular, short-term electricity demand forecasting is essential for the optimal ...
  6. connected at the distribution level, it is also critical to conduct load forecasts at a more granular level (e.g., substation by substation) to ensure that the local network will be able to support all load growth. However, only a few entities in our survey have performed these more localized load forecasts.
  7. Nov 11, 2024This paper compares four time series forecasting algorithms—ARIMA, SARIMA, LSTM, and SVM—suitable for short-term load forecasting using Advanced Metering Infrastructure (AMI) data. The primary focus is on evaluating the applicability and performance of these forecasting models in predicting electricity consumption patterns, which is a critical component for implementing effective demand ...
  8. onlinelibrary.wiley.com

    Other competing models such as support vector regression models, artificial neural network, and recurrent neural network are also used in the analysis. To forecast electricity demand or prices, various researchers combined the characteristics of two or more models and built a novel model generally known as a hybrid model [52 - 58].
  9. researchgate.net

    An accurate forecasting for long-term electricity demand makes a major role in the planning of the power system in any country. Vietnam is one of the most economically developing countries in the ...
  10. ieeexplore.ieee.org

    In our modern world, electricity is of immense importance as it has revolutionized the actual world on every level. Electricity demand forecasting became a key component of every electricity management system as it assists all stakeholders in the process of decision making in order to ensure the reliable generation, transmission, distribution, and consumption of electrical power. Electricity ...

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