Two stage forecast engine with feature selection technique and improved meta-heuristic algorithm for electricity load forecasting

Abstract Short-term load forecasting is of major interest for the restructured environment of the electricity market. Accurate load forecasting is essential for effective power system operation, but electricity load is non-linear with a high level of volatility. Predicting such complex signals requires suitable prediction tools. This paper proposes a hybrid forecast strategy including novel feature selection technique, and a complex forecast engine based on a new intelligent algorithm. The electricity load signal is first filtered by feature selection technique to select appropriate candidates as input for the forecast engine. Then, the proposed two stage forecast engine is implemented based on ridgelet and Elman neural networks. All forecast engine parameters are chosen based on a novel intelligent algorithm to improve its accuracy and capability. Different electricity markets were considered as test cases to compare the proposed method with several current algorithms. Additionally, the proposed forecasting model measures the absolute forecasting errors in this work (among seven types of measurements i.e., absolute forecasting errors, measures based on percentage errors, symmetric errors, measures based on relative errors, scaled errors, relative measures and other error measures). The results validate the effectiveness of the proposed method.

[1]  Noradin Ghadimi,et al.  A new feature selection and hybrid forecast engine for day-ahead price forecasting of electricity markets , 2017, J. Intell. Fuzzy Syst..

[2]  Danica Vukadinovic Greetham,et al.  Long term individual load forecast under different electrical vehicles uptake scenarios , 2015 .

[3]  Ali Deihimi,et al.  Short-term electric load and temperature forecasting using wavelet echo state networks with neural reconstruction , 2013 .

[4]  Chao-Ming Huang,et al.  A particle swarm optimization to identifying the ARMAX model for short-term load forecasting , 2005 .

[5]  Nima Amjady,et al.  Solar energy forecasting based on hybrid neural network and improved metaheuristic algorithm , 2018, Comput. Intell..

[6]  Mohsen Mohammadi,et al.  Small-Scale Building Load Forecast based on Hybrid Forecast Engine , 2017, Neural Processing Letters.

[7]  Nima Amjady,et al.  Short-term load forecast of electrical power system by radial basis function neural network and new stochastic search algorithm , 2016 .

[8]  Shuyuan Yang,et al.  A linear ridgelet network , 2009, Neurocomputing.

[9]  Aref Jalili,et al.  Hybrid harmony search algorithm and fuzzy mechanism for solving congestion management problem in an electricity market , 2016, Complex..

[10]  Jian Wang,et al.  Short, medium and long term load forecasting model and virtual load forecaster based on radial basis function neural networks , 2010 .

[11]  Oveis Abedinia,et al.  A new stochastic search algorithm bundled honeybee mating for solving optimization problems , 2014, Neural Computing and Applications.

[12]  Noradin Ghadimi,et al.  Short-term management of hydro-power systems based on uncertainty model in electricity markets , 2015 .

[13]  D. K. Chaturvedi Soft Computing: Applications to Electrical Engineering Problem , 2007 .

[14]  Ali Deihimi,et al.  Application of echo state networks in short-term electric load forecasting , 2012 .

[15]  M Hanmandlu,et al.  Load Forecasting Using Hybrid Models , 2011, IEEE Transactions on Power Systems.

[16]  Alireza Nouri,et al.  Planning in Microgrids With Conservation of Voltage Reduction , 2018, IEEE Systems Journal.

[17]  Rob J Hyndman,et al.  Short-Term Load Forecasting Based on a Semi-Parametric Additive Model , 2012, IEEE Transactions on Power Systems.

[18]  Adel Akbarimajd,et al.  A new prediction model based on multi-block forecast engine in smart grid , 2018, J. Ambient Intell. Humaniz. Comput..

[19]  Mohsen Mohammadi,et al.  A new multiobjective procedure for solving nonconvex environmental/economic power dispatch , 2014, Complex..

[20]  Qing Song,et al.  On the Weight Convergence of Elman Networks , 2010, IEEE Transactions on Neural Networks.

[21]  Nima Amjady,et al.  Short-term wind power prediction based on Hybrid Neural Network and chaotic shark smell optimization , 2015, International Journal of Precision Engineering and Manufacturing-Green Technology.

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

[23]  Noradin Ghadimi,et al.  The price prediction for the energy market based on a new method , 2018 .

[24]  Mohammad Shahidehpour,et al.  Market operations in electric power systems , 2002 .

[25]  Luis Neves,et al.  Assessing the relevance of load profiling information in electrical load forecasting based on neural network models , 2012 .

[26]  Noradin Ghadimi,et al.  Environmental economic dispatch using improved artificial bee colony algorithm , 2017, Evol. Syst..

[27]  Pedro Paulo Balestrassi,et al.  Electricity demand and spot price forecasting using evolutionary computation combined with chaotic nonlinear dynamic model , 2010 .

[28]  Li Wei,et al.  Based on Time Sequence of ARIMA Model in the Application of Short-Term Electricity Load Forecasting , 2009, 2009 International Conference on Research Challenges in Computer Science.

[29]  Q. Henry Wu,et al.  Electric Load Forecasting Based on Locally Weighted Support Vector Regression , 2010, IEEE Transactions on Systems, Man, and Cybernetics, Part C (Applications and Reviews).

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

[31]  Mohammad Ghiasi,et al.  Extracting Appropriate Nodal Marginal Prices for All Types of Committed Reserve , 2019 .

[32]  Noradin Ghadimi,et al.  PSO Based Fuzzy Stochastic Long-Term Model for Deployment of Distributed Energy Resources in Distribution Systems With Several Objectives , 2013, IEEE Systems Journal.

[33]  Hamidreza Zareipour,et al.  A New Feature Selection Technique for Load and Price Forecast of Electrical Power Systems , 2017, IEEE Transactions on Power Systems.

[34]  A.P. Alves da Silva,et al.  Toward Estimating Autonomous Neural Network-Based Electric Load Forecasters , 2007, IEEE Transactions on Power Systems.

[35]  Noradin Ghadimi,et al.  Fuzzy stochastic long-term model with consideration of uncertainties for deployment of distributed energy resources using interactive honey bee mating optimization , 2014 .

[36]  Noradin Ghadimi,et al.  An analytical methodology for assessment of smart monitoring impact on future electric power distribution system reliability , 2015, Complex..

[37]  Alireza Noruzi,et al.  A new method for probabilistic assessments in power systems, combining monte carlo and stochastic-algebraic methods , 2015, Complex..

[38]  D.R. Hush,et al.  Progress in supervised neural networks , 1993, IEEE Signal Processing Magazine.

[39]  Jie Zhao,et al.  A new Elman neural network and its dynamic properties , 2008, 2008 IEEE Conference on Cybernetics and Intelligent Systems.

[40]  Zhang Min,et al.  RESEARCH ON PROCESSING OF SHORT-TERM HISTORICAL DATA OF DAILY LOAD BASED ON KALMAN FILTER , 2003 .

[41]  Agnaldo J. R. Reis,et al.  Feature extraction via multiresolution analysis for short-term load forecasting , 2005, IEEE Transactions on Power Systems.

[42]  OVEIS ABEDINIA,et al.  A new metaheuristic algorithm based on shark smell optimization , 2016, Complex..