Reconfiguration and reinforcement allocation as applied to hourly medium‐term load forecasting of distribution feeders

Paredes, Gonzalo; Vargas, Luis; Maldonado, Sebastián

doi:10.1049/iet-gtd.2018.7127

Cited by 5 publications

(2 citation statements)

References 37 publications

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“…In comparing the three classifiers, logistic regression was better than the other two methods 72 . The deep learning methods have been applied to the distribution feeders for load forecasting 74 . Jogunola et al 75 assessed energy usage in commercial buildings in a post-COVID-19 environment while investigating the influence of digitization to uncover potential new opportunities using actual power consumption data.…”

Section: Medium-term Load Forecast (Mtlf)mentioning

confidence: 99%

Short term energy consumption forecasting using neural basis expansion analysis for interpretable time series

Shaikh

Nazir

Khan

et al. 2022

Sci Rep

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Smart grids and smart homes are getting people’s attention in the modern era of smart cities. The advancements of smart technologies and smart grids have created challenges related to energy efficiency and production according to the future demand of clients. Machine learning, specifically neural network-based methods, remained successful in energy consumption prediction, but still, there are gaps due to uncertainty in the data and limitations of the algorithms. Research published in the literature has used small datasets and profiles of primarily single users; therefore, models have difficulties when applied to large datasets with profiles of different customers. Thus, a smart grid environment requires a model that handles consumption data from thousands of customers. The proposed model enhances the newly introduced method of Neural Basis Expansion Analysis for interpretable Time Series (N-BEATS) with a big dataset of energy consumption of 169 customers. Further, to validate the results of the proposed model, a performance comparison has been carried out with the Long Short Term Memory (LSTM), Blocked LSTM, Gated Recurrent Units (GRU), Blocked GRU and Temporal Convolutional Network (TCN). The proposed interpretable model improves the prediction accuracy on the big dataset containing energy consumption profiles of multiple customers. Incorporating covariates into the model improved accuracy by learning past and future energy consumption patterns. Based on a large dataset, the proposed model performed better for daily, weekly, and monthly energy consumption predictions. The forecasting accuracy of the N-BEATS interpretable model for 1-day-ahead energy consumption with “day as covariates” remained better than the 1, 2, 3, and 4-week scenarios.

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Section: Medium-term Load Forecast (Mtlf)mentioning

confidence: 99%

Short term energy consumption forecasting using neural basis expansion analysis for interpretable time series

Shaikh

Nazir

Khan

et al. 2022

Sci Rep

View full text Add to dashboard Cite

show abstract

“…In recent years, LA countries have contributed novel proposals in Demand Side Management, forecasting models, and theoretical studies for forecasting optimization (Cruz, Alvarez, Al-Sumaiti, & Rivera, 2020;Cruz, Alvarez, Rivera, & Herrera, 2019;Diaz, Vuelvas, Ruiz, & Patino, 2019;Garcia-Guarin et al, 2019;J. Garcia, Alvarez, & Rivera, 2020; J. R. Garcia, Zambrano P, & Duarte, 2018;Henríquez & Kristjanpoller, 2019;Hernandez & Baeza, 2019;Jiménez, Pertuz, Quintero, & Montaña, 2019;Marrero, García-Santander, Carrizo, & Ulloa, 2019;Moret, Babonneau, Bierlaire, & Maréchal, 2020;Paredes, Vargas, & Maldonado, 2020;Ramirez, Cruz, & Gutierrez, 2019;Rocha, Silvestre, Celeste, Coura, & Rigo, 2018;Romero-Quete & Canizares, 2019;Sanhueza & Freitas, 2018;Zavadzki, Kleina, Drozda, & Marques, 2020;Zuniga-Garcia, Santamaría-Bonfil, Arroyo-Figueroa, & Batres, 2019).…”

Section: Smart Buildings Forecasting Techniquesmentioning

confidence: 99%

Mejoras al desempeño energético en edificaciones abordando los desafíos actuales del demand-side: Una revisión de contribuciones de Latinoamérica

2020

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Debido a la actual crisis energética mundial y como resultado de los acuerdos de las naciones participantes, se han establecido medidas por las Naciones Unidas para superar los desafíos relacionados. A pesar de los esfuerzos para incluir a los países subdesarrollados en dicho proceso de decisión, la mayoría de las contribuciones continúan estando inclinadas al hemisferio norte. Así, este trabajo se enfoca en destacar los esfuerzos realizados por los países Latinoamericanos (LA), entre 2018-2020, para contribuir específicamente en las mejoras en el desempeño energético en edificaciones para abordar los desafíos actuales del lado de la demanda. Dichos desafíos están relacionados con la gestión de la demanda: (i) picos de demanda no controlados y (ii) capacidad de transmisión y distribución insuficiente en la red eléctrica. Las contribuciones de LA se clasifican en independientes, colaboración y aplicación. Los estudios también se clasificaron en teóricos, experimentales, ambos y revisiones. La metodología de filtrado de dos etapas implementada dio como resultado un total de 176 documentos como lista inicial. Al centrarse sólo en los aspectos relacionados con los ocupantes, las soluciones pasivas y de bajo consumo y las técnicas de previsión para edificios inteligentes, la lista procesada dio como resultado un total de 73 estudios. Los resultados mostraron que los esfuerzos realizados por los países LA residen en su mayoría en la implementación de estrategias previamente desarrolladas y propuestas por países desarrollados, para realizar estudios de caso como independiente o en colaboración. Finalmente, se presenta un análisis FODA para analizar más los resultados.

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Saturated load forecasting based on clustering and logistic iterative regression

Ran

Xue

Wang

et al. 2022

Electric Power Systems Research

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Reconfiguration and reinforcement allocation as applied to hourly medium‐term load forecasting of distribution feeders

Cited by 5 publications

References 37 publications

Short term energy consumption forecasting using neural basis expansion analysis for interpretable time series

Short term energy consumption forecasting using neural basis expansion analysis for interpretable time series

Mejoras al desempeño energético en edificaciones abordando los desafíos actuales del demand-side: Una revisión de contribuciones de Latinoamérica

Saturated load forecasting based on clustering and logistic iterative regression

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