study of neural networks for electric power load forecasting

Univ Electr Sci & Technol China, Chinese Univ Hong Kong, Asia Pacific Neural Network Assembly, European Neural Network Soc, IEEE Circuits & Syst Soc, IEEE Computat Intelligence Soc, Int Neural Network Soc, Natl Nat Sci Fdn China, KC Wong EducElectric Power Load Forecasting is important for the economic and secure operation of power system, and highly accurate forecasting result leads to substantial savings in operating cost and increased reliability of power supply. Conventional

Stochastic and Artificial Intelligence-based Methods for the Evaluation of Electricity Demand Forecast. Load Forecasting During COVID-19 Pandemic = Métodos estocásticos y basados en inteligencia artificial para la evaluación de pronósticos de demanda de electricidad. Pronóstico de carga durante la pandemia COVID-19

Abstract: Stochastic time series methods for load forecasting are the traditional methods used by electric power companies [1] because they can model seasonal and stochastic behavior of the load [2]. A load time series has patterns that repeat at different time scales such as daily, weekly or yearly. These patterns indicate that load can be predicted and the shortest seasonality component has the highest effect on the forecast. There might be conditions in which load is in different time resolutions, has a different amount of missing data or is under special events such as COVID-19 pandemic. Load forecasting methods should be able to perform well under these circumstances. On the other hand, neural networks are a wide research topic, they can learn [3] and have low forecasting errors [4]. In this work we explain the main characteristics of load demand in time, load forecasting and we compare stochas-tic time series methods with neural networks for load forecasting in terms of the error with respect to the real load. Resumen: Los métodos de series de tiempo estocásticos para pronósticos de carga son los métodos tradicionales usados por compañías de energía eléctrica [1] porque pueden modelar el comportamiento estocástico y estacional de la carga [2]. Una serie de tiempo de carga tiene patrones que se repiten en diferentes escalas de tiempo como diaria, semanal o anual. Estos patrones indican que la carga puede ser predicha y que el componente de estacionalidad más corto tiene el efecto más grande en el pronóstico. Pude que haya condiciones en las cuales la carga este en diferentes resoluciones de tiempo, tenga una diferente cantidad de datos faltantes o este bajo eventos especiales como la pandemia COVID-19. Los métodos de pronóstico de carga deberían ser capaces de llevarse a cabo bien bajo estas circunstancias. Por otra parte, las redes neuronales son un tema de investigación amplio, ellas pueden aprender [3] y tienen errores de pronóstico bajos [4]. En este trabajo explicamos las principales características de la demanda de carga en el tiempo, pronósticos de carga y comparamos métodos de serie de tiempo estocásticos con redes neuronales para pronósticos de carga en términos del error con respecto a la carga real. [1] H. Wang, B.-S. Li, X.-Y. Han, D.-L. Wang and H. Jin, "Study of Neural Networks for Electric Power Load Forecasting," Third International Symposium on Neural Networks - Advances in Neural Networks - ISNN 2006, Proceedings, Part II, pp. 1277-1283, 2006. [2] K. Berk, Modeling and Forecasting Electricity Demand, Siegen: Springer Spektrum, 2015. [3] H. Alfares and N. Mohammad, "Electric load forecasting: Literature survey and classification of methods," International Journal of Systems Science - IJSySc. 33, vol. 33, no. 1, pp. 23-34, 2002. [4] M. Singh and R. Maini, "Various Electricity Load Forecasting Techniques with Pros and Cons," International Journal of Recent Technology and Engineering (IJRTE), vol. 8, pp. 220-229, 2020