ELS-Net: A New Approach to Forecast Decomposed Intrinsic Mode Functions of Electricity Load

Nadeem Javaid,Adia Khalid,Aqdas Naz,Muhammad Shafiq,Rabiya Khalid,Ahmad Almogren

doi:10.1109/access.2020.3034113

Nadeem Javaid, Adia Khalid + Show 4 more

Open Access

https://doi.org/10.1109/access.2020.3034113

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Abstract

The significance of electricity cannot be overlooked as all fields of life like material production, health care, educational sector, etc., depend upon it to render consistent and high-quality services, increase productivity and business continuity. To this end, energy operators have experienced a continuous increasing trend in the electricity demand for the past few decades. This may cause many issues like load shedding, increased electricity bills, imbalance between supply and demand, etc. Therefore, forecasting of electricity demand using efficient techniques is crucial for the energy operators to decide about optimal unit commitment and to make electricity dispatch plans. It also helps to avoid wastage as well as the shortage of energy. In this study, a novel forecasting model, known as ELS-net is proposed, which is a combination of an Ensemble Empirical Mode Decomposition (EEMD) method, multi-model Ensemble Bi Long Short-Term Memory (EBiLSTM) forecasting technique and Support Vector Machine (SVM). In the proposed model, EEMD is used to distinguish between linear and non-linear intrinsic mode functions (IMFs), EBiLSTM is used to forecast the non-linear IMFs and SVM is employed to forecast the linear IMFs. Using separate forecasting techniques for linear and non-linear IMFs decreases the computational complexity of the model. Moreover, SVM requires low computational time as compared to EBiLSTM for linear IMFs. Simulations are performed to examine the effectiveness of the proposed model using two different datasets: New South Wales (NSW) and Victoria (VIC). For performance evaluation, Root Mean Square Error (RMSE), Mean Absolute Error (MAE) and Mean Absolute Percentage Error (MAPE) are used as performance metrics. From the simulation results, it is obvious that the proposed ELS-net model outperforms the start-of-the-art techniques, such as EMD-BILSTM-SVM, EMD-PSO-GA-SVR, BiLSTM, MLP and SVM in terms of forecasting accuracy and minimum execution time.

Highlights

T HE accurate and efficient load forecasting helps the power companies to perform enhanced grid construction planning
New South Wales (NSW) and VIC are distinct regional areas. These are connected to Queen Land (QLD) using interconnecting transmission lines
In order to analyze variations in electricity consumption, four different seasons are taken into account

Summary

Introduction

T HE accurate and efficient load forecasting helps the power companies to perform enhanced grid construction planning. It assists in the improvement of economic and social benefits of the power systems. The accurately forecasted data is very effective in predicting the potential faults of the system. Security of the power grids can be ensured using predicted data. Load forecasting helps to ensure the balance between supply and demand of electricity. Energy generation can be managed without wasting or generating surplus energy. A detailed analysis is performed on the time series data, which aims to extract meaningful statistics and other important features of the time series [1]. Time series is analyzed using two main methods, i.e., univariate and multivariate [2]

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