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.
Highlights
With a single hidden layer, over the traditional statistical linear parametric models, such as Auto-Regressive with Exogenous Inputs (ARX), Auto-Regressive Moving Average with Exogenous Inputs (ARMAX) and Output Error (OE), this section presents a comprehensive overview of quantitative and qualitative analysis based on the evaluation metrics as discussed below
The following key performance metrics are used to evaluate the model’s performance: Mean Absolute Percentage Error (MAPE), Mean Absolute Error (MAE), Root Mean Square Error (RMSE), R-square value and standard deviation, and the relevant formulas are given in Equations (15)–(19) [57]
This research paper discusses the effectiveness of eight different state-of-the-art linear and non-linear parametric methodologies, i.e., OE, ARX, ARMAX, K-Nearest Neighbor (KNN), Bagged Trees, Support Vector Machine (SVM), NN–Particle Swarm Optimization (PSO), a single hidden layer Artificial Neural Network (ANN)–LM and a two hidden layer ANN–LM, on real-time electrical load data for Short-Term Electrical Load Forecasting (STLF) problems
Summary
Short-Term Electrical Load Forecasting (STLF) is used by planning authorities to forecast energy demand, ranging from one hour to one week ahead [1,2]. Security and protection of electrical power systems can be malfunctioned by irregular power flows and system congestion due to an inaccurate electrical load forecast, which may lead towards imbalanced generation planning. Electricity generation, transmission and distribution networks governed by electric utilities over the world need an accurate STLF for reliable and economical short-term operations of power systems [6]. The inspiration behind this research work is to empower the planning authorities of electric utilities with state-of-the-art linear and non-linear parametric methodologies since linear and non-linear parametric methodologies are more suitable to handle the system dynamics and non-linearities [8]
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