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

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.

[1]  Jürgen Schmidhuber,et al.  Long Short-Term Memory , 1997, Neural Computation.

[2]  M. Parti,et al.  The Total and Appliance-Specific Conditional Demand for Electricity in the Household Sector , 1980 .

[3]  Mashor Housh,et al.  The Advantage of Using International Multimodel Ensemble for Seasonal Precipitation Forecast over Israel , 2017 .

[4]  R. M. Nelms,et al.  Ensemble Learning for Load Forecasting , 2020, IEEE Transactions on Green Communications and Networking.

[5]  Qianqian Wu,et al.  A Hybrid Forecasting Model Based on EMD-GASVM-RBFNN for Power Grid Investment Demand , 2018, Mathematical Problems in Engineering.

[6]  Girijesh Prasad,et al.  EWMA model based shift-detection methods for detecting covariate shifts in non-stationary environments , 2015, Pattern Recognit..

[7]  Jun Hu,et al.  Short-Term Load Forecasting With Deep Residual Networks , 2018, IEEE Transactions on Smart Grid.

[8]  Jaime Lloret,et al.  Artificial neural networks for short-term load forecasting in microgrids environment , 2014 .

[9]  Ratnadip Adhikari,et al.  Time Series Forecasting Using Hybrid ARIMA and ANN Models Based on DWT Decomposition , 2015 .

[10]  M. E. Günay,et al.  Forecasting annual gross electricity demand by artificial neural networks using predicted values of socio-economic indicators and climatic conditions: Case of Turkey , 2016 .

[11]  Wei-Chiang Hong,et al.  Chaotic particle swarm optimization algorithm in a support vector regression electric load forecasting model , 2009 .

[12]  Keegan J. Moore,et al.  Wavelet-bounded empirical mode decomposition for measured time series analysis , 2018 .

[13]  Nadeem Javaid,et al.  Exploiting Deep Learning for Wind Power Forecasting Based on Big Data Analytics , 2019, Applied Sciences.

[14]  Yitao Liang,et al.  A novel bearing fault diagnosis model integrated permutation entropy, ensemble empirical mode decomposition and optimized SVM , 2015 .

[15]  Akihiko Takahashi,et al.  Generalized exponential moving average (EMA) model with particle filtering and anomaly detection , 2016, Expert Syst. Appl..

[16]  Jia-Qi Zhu,et al.  Improved EEMD-based crude oil price forecasting using LSTM networks , 2019, Physica A: Statistical Mechanics and its Applications.

[17]  Amaury Lendasse,et al.  Adaptive Ensemble Models of Extreme Learning Machines for Time Series Prediction , 2009, ICANN.

[18]  Nadeem Javaid,et al.  ESAENARX and DE-RELM: Novel schemes for big data predictive analytics of electricity load and price , 2019, Sustainable Cities and Society.

[19]  Ponnuthurai Nagaratnam Suganthan,et al.  Empirical Mode Decomposition based ensemble deep learning for load demand time series forecasting , 2017, Appl. Soft Comput..

[20]  Tanveer Ahmad,et al.  Deployment of data-mining short and medium-term horizon cooling load forecasting models for building energy optimization and management , 2019, International Journal of Refrigeration.

[21]  Nadeem Javaid,et al.  Towards Modified Entropy Mutual Information Feature Selection to Forecast Medium-Term Load Using a Deep Learning Model in Smart Homes , 2020, Entropy.

[22]  Mayur Barman,et al.  A regional hybrid GOA-SVM model based on similar day approach for short-term load forecasting in Assam, India , 2018 .

[23]  Yusuf Yaslan,et al.  Empirical mode decomposition based denoising method with support vector regression for time series prediction: A case study for electricity load forecasting , 2017 .

[24]  Yan Li,et al.  Short-term electricity demand forecasting with MARS, SVR and ARIMA models using aggregated demand data in Queensland, Australia , 2018, Adv. Eng. Informatics.

[25]  Yanfei Li,et al.  Comparison of two new intelligent wind speed forecasting approaches based on Wavelet Packet Decomposition, Complete Ensemble Empirical Mode Decomposition with Adaptive Noise and Artificial Neural Networks , 2018 .

[26]  Nadeem Javaid,et al.  Electricity Load and Price Forecasting Using Jaya-Long Short Term Memory (JLSTM) in Smart Grids , 2019, Entropy.

[27]  Sílvio Mariano,et al.  A bat optimized neural network and wavelet transform approach for short-term price forecasting , 2018 .

[28]  Roland Fried,et al.  Exponential and Holt-Winters Smoothing , 2011, International Encyclopedia of Statistical Science.

[29]  Li Li,et al.  A new method based on Type-2 fuzzy neural network for accurate wind power forecasting under uncertain data , 2018, Renewable Energy.

[30]  Maria Lindén,et al.  A Deep Machine Learning Method for Classifying Cyclic Time Series of Biological Signals Using Time-Growing Neural Network , 2018, IEEE Transactions on Neural Networks and Learning Systems.

[31]  Durga Toshniwal,et al.  Empirical Mode Decomposition Based Deep Learning for Electricity Demand Forecasting , 2018, IEEE Access.

[32]  Eric Wai Ming Lee,et al.  Novel dynamic forecasting model for building cooling loads combining an artificial neural network and an ensemble approach , 2018, Applied Energy.

[33]  Danilo P. Mandic,et al.  Recurrent Neural Networks for Prediction: Learning Algorithms, Architectures and Stability , 2001 .

[34]  Le Zhang,et al.  Ensemble deep learning for regression and time series forecasting , 2014, 2014 IEEE Symposium on Computational Intelligence in Ensemble Learning (CIEL).

[35]  Wei-Chiang Hong,et al.  Applications of Hybrid EMD with PSO and GA for an SVR-Based Load Forecasting Model , 2017 .

[36]  Norden E. Huang,et al.  Ensemble Empirical Mode Decomposition: a Noise-Assisted Data Analysis Method , 2009, Adv. Data Sci. Adapt. Anal..

[37]  Nadeem Javaid,et al.  Towards efficient energy management in smart grids considering microgrids with day-ahead energy forecasting , 2020 .

[38]  Junzo Watada,et al.  Short-Term Load Forecasting Using Optimized LSTM Networks Based on EMD , 2018, 2018 10th International Conference on Communications, Circuits and Systems (ICCCAS).

[39]  Heng-Ming Tai,et al.  An Optimized Heterogeneous Structure LSTM Network for Electricity Price Forecasting , 2019, IEEE Access.

[40]  Fuad E. Alsaadi,et al.  A switching delayed PSO optimized extreme learning machine for short-term load forecasting , 2017, Neurocomputing.

[41]  V. Ismet Ugursal,et al.  Comparison of neural network, conditional demand analysis, and engineering approaches for modeling end-use energy consumption in the residential sector , 2008 .

[42]  M. D. Mainar-Toledo,et al.  Multiple regression models to predict the annual energy consumption in the Spanish banking sector , 2012 .

[43]  Bo Zhang,et al.  Constructing a PM2.5 concentration prediction model by combining auto-encoder with Bi-LSTM neural networks , 2020, Environ. Model. Softw..

[44]  David J. Hill,et al.  Short-Term Residential Load Forecasting Based on Resident Behaviour Learning , 2018, IEEE Transactions on Power Systems.