Deterioration of Electrical Load Forecasting Models in a Smart Grid Environment

Sensors

Kamaluddeen Usman Danyaro¹  Saurabh Shukla²  Abdul Azeem³  Idris Ismail³  Fakhizan Romlie³  Syed Muslim Jameel⁴ 

[1] Computer Science Department, Universiti Teknologi PETRONAS, Seri Iskandar 32610, Malaysia;Data Science Institute (DSI), National University of Ireland Galway (NUIG), Galway H91 TK33, Ireland;Electrical and Electronics Engineering Department, Universiti Teknologi PETRONAS, Seri Iskandar 32610, Malaysia;Postdoc Scientist at Structure Lab, School of Engineering, National University of Ireland Galway (NUIG), Galway H91 TK33, Ireland;

关键词: energy management;    adaptive models;    generation modalities;    load forecasting;    machine learning;    model deterioration;   

DOI  :  10.3390/s22124363

来源: DOAJ

【 摘 要 】

Smart Grid (S.G.) is a digitally enabled power grid with an automatic capability to control electricity and information between utility and consumer. S.G. data streams are heterogenous and possess a dynamic environment, whereas the existing machine learning methods are static and stand obsolete in such environments. Since these models cannot handle variations posed by S.G. and utilities with different generation modalities (D.G.M.), a model with adaptive features must comply with the requirements and fulfill the demand for new data, features, and modality. In this study, we considered two open sources and one real-world dataset and observed the behavior of ARIMA, ANN, and LSTM concerning changes in input parameters. It was found that no model observed the change in input parameters until it was manually introduced. It was observed that considered models experienced performance degradation and deterioration from 5 to 15% in terms of accuracy relating to parameter change. Therefore, to improve the model accuracy and adapt the parametric variations, which are dynamic in nature and evident in S.G. and D.G.M. environments. The study has proposed a novel adaptive framework to overcome the existing limitations in electrical load forecasting models.

【 授权许可】

Unknown