Exploratory Data Analysis Based Short-Term Electrical Load Forecasting: A Comprehensive Analysis

Umar Javed, Khalid Ijaz, Muhammad Jawad, Ejaz A. Ansari, Noman Shabbir, Lauri Kütt and Oleksandr Husev
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Umar Javed: Department of Electrical and Computer Engineering, COMSATS University Islamabad, Lahore 54000, Pakistan
Khalid Ijaz: Electrical Engineering Department, University of Management and Technology, Lahore 54000, Pakistan
Muhammad Jawad: Department of Electrical and Computer Engineering, COMSATS University Islamabad, Lahore 54000, Pakistan
Ejaz A. Ansari: Department of Electrical and Computer Engineering, COMSATS University Islamabad, Lahore 54000, Pakistan
Noman Shabbir: Department of Electrical Power Engineering & Mechatronics, Tallinn University of Technology, 12616 Tallinn, Estonia
Lauri Kütt: Department of Electrical Power Engineering & Mechatronics, Tallinn University of Technology, 12616 Tallinn, Estonia
Oleksandr Husev: Department of Electrical Power Engineering & Mechatronics, Tallinn University of Technology, 12616 Tallinn, Estonia

Energies, 2021, vol. 14, issue 17, 1-22

Abstract: Power system planning in numerous electric utilities merely relies on the conventional statistical methodologies, such as ARIMA for short-term electrical load forecasting, which is incapable of determining the non-linearities induced by the non-linear seasonal data, which affect the electrical load. This research work presents a comprehensive overview of modern linear and non-linear parametric modeling techniques for short-term electrical load forecasting to ensure stable and reliable power system operations by mitigating non-linearities in electrical load data. Based on the findings of exploratory data analysis, the temporal and climatic factors are identified as the potential input features in these modeling techniques. The real-time electrical load and meteorological data of the city of Lahore in Pakistan are considered to analyze the reliability of different state-of-the-art linear and non-linear parametric methodologies. Based on performance indices, such as Root Mean Square Error (RMSE), Mean Absolute Percentage Error (MAPE) and Mean Absolute Error (MAE), the qualitative and quantitative comparisons have been conferred among these scientific rationales. The experimental results reveal that the ANN–LM with a single hidden layer performs relatively better in terms of performance indices compared to OE, ARX, ARMAX, SVM, ANN–PSO, KNN, ANN–LM with two hidden layers and bootstrap aggregation models.

Keywords: short-term load forecasting; time-series forecasting; exploratory data analysis; neural network; Levenberg–Marquardt (search for similar items in EconPapers)
JEL-codes: Q Q0 Q4 Q40 Q41 Q42 Q43 Q47 Q48 Q49 (search for similar items in EconPapers)
Date: 2021
References: View references in EconPapers View complete reference list from CitEc
Citations: View citations in EconPapers (7)

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