Research and application of a hybrid forecasting framework based on multi-objective optimization for electrical power system

Abstract Electrical power system (EPS) forecasting plays a significant role in economic and social development but it remains an extremely challenging task. Because of its significance, relevant studies on EPS are especially needed. More specifically, only a few of the previous studies in this area conducted in-depth investigations of the entire EPS forecasting and merely focused on modeling individual signals centered on wind speed or electrical load. Moreover, most of these past studies concentrated on accuracy improvements and usually ignore the significance of forecasting stability. Therefore, to simultaneously achieve high accuracy and dependable stability, a hybrid forecasting framework based on the multi-objective dragonfly algorithm (MODA) was successfully developed in this study. The framework consists of four modules—data preprocessing, optimization, forecasting, and evaluation modules. In this framework, MODA is employed to optimize the Elman neural network (ENN) model as a part of the optimization module to overcome the drawbacks of single-objective optimization algorithms. In addition, data preprocessing and evaluation modules are incorporated to improve forecasting performance and conduct a comprehensive evaluation for this framework, respectively. Empirical results reveal that the developed forecasting framework can be an effective tool for the planning and management of power grids.

[1]  Jianzhou Wang,et al.  Research and application of a novel hybrid forecasting system based on multi-objective optimization for wind speed forecasting , 2017 .

[2]  Haikun Wei,et al.  A Gaussian process regression based hybrid approach for short-term wind speed prediction , 2016 .

[3]  Kostas S. Metaxiotis,et al.  Artificial intelligence in short term electric load forecasting: a state-of-the-art survey for the researcher , 2003 .

[4]  Ming Chui Dong,et al.  A novel random fuzzy neural networks for tackling uncertainties of electric load forecasting , 2015 .

[5]  Jianzhou Wang,et al.  A self-adaptive hybrid approach for wind speed forecasting , 2015 .

[6]  Luca Delle Monache,et al.  Comparison of numerical weather prediction based deterministic and probabilistic wind resource assessment methods , 2015 .

[7]  J. Contreras,et al.  ARIMA models to predict next-day electricity prices , 2002 .

[8]  Jeffrey L. Elman,et al.  Finding Structure in Time , 1990, Cogn. Sci..

[9]  F. Diebold,et al.  Comparing Predictive Accuracy , 1994, Business Cycles.

[10]  Conor Lynch,et al.  Simplified Method to Derive the Kalman Filter Covariance Matrices to Predict Wind Speeds from a NWP Model , 2014 .

[11]  Wei-Chiang Hong,et al.  Electric load forecasting by seasonal recurrent SVR (support vector regression) with chaotic artific , 2011 .

[12]  Whei-Min Lin,et al.  Unbalanced distribution network fault analysis with hybrid compensation , 2011 .

[13]  Abbas Khosravi,et al.  A review on artificial intelligence based load demand forecasting techniques for smart grid and buildings , 2015 .

[14]  Whei-Min Lin,et al.  Hybrid Control of a Wind Induction Generator Based on Grey–Elman Neural Network , 2013, IEEE Transactions on Control Systems Technology.

[15]  Gabriel Rilling,et al.  Empirical mode decomposition as a filter bank , 2004, IEEE Signal Processing Letters.

[16]  Chen Wang,et al.  Research and application of a hybrid model based on multi-objective optimization for electrical load forecasting , 2016 .

[17]  Teuku Meurah Indra Mahlia,et al.  Optimization of transesterification process for Ceiba pentandra oil: A comparative study between kernel-based extreme learning machine and artificial neural networks , 2017 .

[18]  Wei Wang,et al.  Electricity load forecasting by an improved forecast engine for building level consumers , 2017 .

[19]  Andrew Lewis,et al.  Novel performance metrics for robust multi-objective optimization algorithms , 2015, Swarm Evol. Comput..

[20]  Jianzhou Wang,et al.  A corrected hybrid approach for wind speed prediction in Hexi Corridor of China , 2011 .

[21]  José M. Matías,et al.  Influence of the input layer signals of ANNs on wind power estimation for a target site: A case study , 2011 .

[22]  Zhang Yang,et al.  Electricity price forecasting by a hybrid model, combining wavelet transform, ARMA and kernel-based extreme learning machine methods , 2017 .

[23]  Daniel Ambach,et al.  A new high-dimensional time series approach for wind speed, wind direction and air pressure forecasting , 2017, 1707.03258.

[24]  Jianzhou Wang,et al.  Analysis and forecasting of the oil consumption in China based on combination models optimized by artificial intelligence algorithms , 2018 .

[25]  A. Alessandri,et al.  Electricity demand forecasting over Italy: Potential benefits using numerical weather prediction models , 2013 .

[26]  Jinxing Che,et al.  An incremental electric load forecasting model based on support vector regression , 2016 .

[27]  Feng Liu,et al.  A hybrid forecasting model based on date-framework strategy and improved feature selection technology for short-term load forecasting , 2017 .

[28]  Ting-Chia Ou.  Ground fault current analysis with a direct building algorithm for microgrid distribution , 2013 .

[29]  Erasmo Cadenas,et al.  Wind speed forecasting in three different regions of Mexico, using a hybrid ARIMA–ANN model , 2010 .

[30]  Chiou-Jye Huang,et al.  Improvement of Transient Stability in a Hybrid Power Multi-System Using a Designed NIDC (Novel Intelligent Damping Controller) , 2017 .

[31]  María Eugenia Torres,et al.  Improved complete ensemble EMD: A suitable tool for biomedical signal processing , 2014, Biomed. Signal Process. Control..

[32]  Ting-Chia Ou,et al.  A novel unsymmetrical faults analysis for microgrid distribution systems , 2012 .

[33]  Rui Wang,et al.  Research and Application of a Novel Hybrid Model Based on Data Selection and Artificial Intelligence Algorithm for Short Term Load Forecasting , 2017, Entropy.

[34]  Chen Wang,et al.  Research and application of a combined model based on multi-objective optimization for multi-step ahead wind speed forecasting , 2017 .

[35]  Haiyan Lu,et al.  Comprehensive assessment of wind resources and the low-carbon economy: An empirical study in the Alxa and Xilin Gol Leagues of inner Mongolia, China , 2015 .

[36]  Carlos A. Coello Coello,et al.  Evolutionary multi-objective optimization: some current research trends and topics that remain to be explored , 2009, Frontiers of Computer Science in China.

[37]  Jianzhou Wang,et al.  Multi-step-ahead wind speed forecasting based on optimal feature selection and a modified bat algorithm with the cognition strategy , 2018 .

[38]  Chih-Ming Hong,et al.  Development of intelligent MPPT (maximum power point tracking) control for a grid-connected hybrid power generation system , 2013 .

[39]  Ioannis P. Panapakidis,et al.  Day-ahead electricity price forecasting via the application of artificial neural network based models , 2016 .

[40]  M.A. El-Sharkawi,et al.  Pareto Multi Objective Optimization , 2005, Proceedings of the 13th International Conference on, Intelligent Systems Application to Power Systems.

[41]  Yu Jin,et al.  A generalized dynamic fuzzy neural network based on singular spectrum analysis optimized by brain storm optimization for short-term wind speed forecasting , 2017, Appl. Soft Comput..

[42]  He Jiang,et al.  Global horizontal radiation forecast using forward regression on a quadratic kernel support vector machine: Case study of the Tibet Autonomous Region in China , 2017 .

[43]  Dan Wang,et al.  Power system operation risk analysis considering charging load self-management of plug-in hybrid electric vehicles , 2014 .

[44]  N. Huang,et al.  The empirical mode decomposition and the Hilbert spectrum for nonlinear and non-stationary time series analysis , 1998, Proceedings of the Royal Society of London. Series A: Mathematical, Physical and Engineering Sciences.

[45]  Patrick Flandrin,et al.  A complete ensemble empirical mode decomposition with adaptive noise , 2011, 2011 IEEE International Conference on Acoustics, Speech and Signal Processing (ICASSP).

[46]  Marek Brabec,et al.  Structured, physically inspired (gray box) models versus black box modeling for forecasting the output power of photovoltaic plants , 2017 .

[47]  Yunzhen Xu,et al.  Air quality early-warning system for cities in China , 2017 .

[48]  C.A. Coello Coello,et al.  MOPSO: a proposal for multiple objective particle swarm optimization , 2002, Proceedings of the 2002 Congress on Evolutionary Computation. CEC'02 (Cat. No.02TH8600).

[49]  S. Fan,et al.  Short-term load forecasting based on an adaptive hybrid method , 2006, IEEE Transactions on Power Systems.

[50]  Chia-Nan Ko,et al.  Short-term load forecasting using lifting scheme and ARIMA models , 2011, Expert Syst. Appl..

[51]  Jianzhou Wang,et al.  A novel hybrid system based on a new proposed algorithm-Multi-Objective Whale Optimization Algorithm for wind speed forecasting , 2017 .

[52]  Hui Liu,et al.  Wind speed forecasting approach using secondary decomposition algorithm and Elman neural networks , 2015 .

[53]  Maria Grazia De Giorgi,et al.  Assessment of the benefits of numerical weather predictions in wind power forecasting based on stati , 2011 .

[54]  Chia-Feng Juang,et al.  A hybrid of genetic algorithm and particle swarm optimization for recurrent network design , 2004, IEEE Transactions on Systems, Man, and Cybernetics, Part B (Cybernetics).

[55]  Ping Jiang,et al.  A hybrid forecasting approach applied in the electrical power system based on data preprocessing, optimization and artificial intelligence algorithms , 2016 .

[56]  Jianzhou Wang,et al.  Short-term wind speed forecasting using a hybrid model , 2017 .

[57]  Robert P. Broadwater,et al.  Current status and future advances for wind speed and power forecasting , 2014 .

[58]  Olivier Grunder,et al.  Multi-step ahead electricity price forecasting using a hybrid model based on two-layer decomposition technique and BP neural network optimized by firefly algorithm , 2017 .

[59]  Seyedali Mirjalili,et al.  Dragonfly algorithm: a new meta-heuristic optimization technique for solving single-objective, discrete, and multi-objective problems , 2015, Neural Computing and Applications.

[60]  Carlos A. Coello Coello,et al.  Handling multiple objectives with particle swarm optimization , 2004, IEEE Transactions on Evolutionary Computation.

[61]  Jianzhou Wang,et al.  Application of residual modification approach in seasonal ARIMA for electricity demand forecasting: A case study of China , 2012 .

[62]  Shyh-Jier Huang,et al.  A Modified Bird-Mating Optimization with Hill-Climbing for Connection Decisions of Transformers , 2016 .

[63]  Xiao Yang,et al.  Energy optimization and analysis modeling based on extreme learning machine integrated index decomposition analysis: Application to complex chemical processes , 2017 .

[64]  Kang-Ming Chang.  Ensemble empirical mode decomposition for high frequency ECG noise reduction , 2010, Biomedizinische Technik. Biomedical engineering.

[65]  Chen Hua-you.  Research on Superior Combination Forecasting Model Based on Forecasting Effective Measure , 2002 .

[66]  Chih-Ming Hong,et al.  Dynamic operation and control of microgrid hybrid power systems , 2014 .

[67]  Jianzhou Wang,et al.  Research and application of a hybrid model based on dynamic fuzzy synthetic evaluation for establishing air quality forecasting and early warning system: A case study in China. , 2017, Environmental pollution.

[68]  S. SreeRanjiniK.,et al.  Memory based Hybrid Dragonfly Algorithm for numerical optimization problems , 2017, Expert Syst. Appl..

[69]  Nitin Singh,et al.  Short term electricity price forecast based on environmentally adapted generalized neuron , 2017 .

[70]  Yanbin Yuan,et al.  Wind power prediction using hybrid autoregressive fractionally integrated moving average and least square support vector machine , 2017 .

[71]  Yi Zhang,et al.  Application of a hybrid quantized Elman neural network in short-term load forecasting , 2014 .

[72]  Pavlos S. Georgilakis,et al.  Technical challenges associated with the integration of wind power into power systems , 2008 .