Abstract
Intelligent solutions, based on artificial intelligence (AI) technologies, to solve complicated practical problems in various sectors are becoming more and more widespread nowadays. On the other hand, electrical load prediction is one of the important concerns of power systems so development of intelligent prediction tools for performing accurate predictions is essential. This study presents an intelligent hybrid approach called ACO-SA by hybridization of Ant Colony Optimization (ACO) and Simulated Annealing (SA). The hybrid approach consists of two general stages. At the first stage time series inputs will be fed into ACO and it performs a global search to find a globally optimum solution. At the second stage, ACO’s outcome will be fed into SA as initial solution and then SA starts local search around the ACO’s global optimum and performs the tuning process on the initial solution. The superiority and applicability of the ACO-SA approach is shown for Iranian monthly load prediction problem and outcomes of the hybrid method are compared with Single ACO, Single SA and ANN technique (which is a common technique in the field of load prediction). Results show that ACO-SA approach outperforms rest of the methods regarding to prediction accuracy, so it can be considered as a promising alternative for load prediction studies.
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References
Hisham Choueiki, M., Mount-Campbell Clark, A., Ahalt Stanley, C.: Implementing a Weighted Least Squares Procedure in Training a Neural Network to Solve the Short-term Load Forecasting Problem. IEEE Trans. PWRS, 1689–1694 (1997)
Hisham Choueiki, M., Mount-Campbell Clark, A., Ahalt Stanley, C.: Building a ‘quasi optimal’ Neural Network to Solve the Short-term Load Forecasting Problem. IEEE Trans. PWRS, 1432–1439 (1997)
Konar, A.: Computational Intelligence: Principles, techniques. Springer, Berlin (2005)
Niska, H., Hiltunen, T., Karppinen, A., Ruuskanen, J., Kolehmainen, M.: Evolving the Neural Network Model for Forecasting Air pollution time series. Engineering Applications of Artificial Intelligence 17(2), 159–167 (2004)
Hanias, M.P., Karras, D.A.: On Efficient Multistep Non-linear Time Series Prediction in Chaotic Diode Resonator Circuits by Optimizing the Combination of Non-linear Time Series Analysis and Neural Networks. Engineering Applications of Artificial Intelligence 22(1), 32–39 (2009)
Faruk, D.: A Hybrid Neural Network and ARIMA Model for Water Quality Time Series Prediction. Engineering Applications of Artificial Intelligence (2009)
Esmaeil, H., Shavandi, H., Arash, G.: Integration of Genetic Fuzzy Systems and Artificial Neural Networks for Stock Price Forecasting. Knowl.Based Syst. (2010) doi:10.1016/j.knosys.2010.05.004
Ghanbari, A., Naghavi, A., Ghaderi, S.F.: Artificial Neural Networks and Regression Approaches Comparison for Forecasting Iran’s Annual Electricity Load. In: IEEE Second International Conference on Power Engineering, Energy and Electrical Drives, Lisbon, Portugal, pp.675–679 (2009)
Darbellay, G., Slama, M.: Forecasting the Short-term Demand for Electricity Do neural Networks Stand a Better Chance? International Journal of Forecasting 16, 71–83 (2000)
Abraham, A., Nath, B.: A Neuro-fuzzy Approach for Modeling Electricity Demand in Victoria. Applied Soft Computing 1, 127–138 (2001)
Lopes, M., Minussi, C., Lotufo, A.: Electric Load Forecasting Using a Fuzzy ART&ARTMAP Neural Network. Applied Soft Computing 5, 235–244 (2005)
Topalli, A., Erkmen, I., Topalli, I.: Intelligent Short-term Load Forecasting in Turkey. Electrical Power and Energy Systems 28, 437–447 (2006)
Ying, L.-C., Pan, M.-C.: Using Adaptive Network Based Fuzzy Inference System to Forecast Regional Electricity Loads. Energy Conversion and Management 49, 205–211 (2008)
Pao, H.-T.: Comparing Linear and Nonlinear Forecasts for Taiwan’s Electricity Consumption. Energy 31, 2129–2141 (2006)
Hamzacebi, C.: Forecasting of Turkey’s Net Electricity Energy Consumption on Sectoral Bases. Energy Policy 35, 2009–2016 (2007)
Toksarı, M.D.: Ant Colony Optimization for Finding the Global Minimum. Applied Mathematics and Computation 176, 308–316 (2006)
Azadeh, A., Tarverdian, S.: Integration of Genetic Algorithm, Computer Simulation and Design of Experiments for Forecasting Electrical Energy Consumption. Energy Policy 35(10), 5229–5241 (2007)
Palm, R.: Multiple-step-ahead Prediction in Control Systems with Gaussian Process Models and TS-fuzzy Models. Engineering Applications of Artificial Intelligence 20(8), 1023–1035 (2007)
Jamali, A., Nariman-zadeh, N., Darvizeh, A., Masoumi, A., Hamrang, S.: Multi-objective Evolutionary Optimization of Polynomial Neural Networks for Modelling and Prediction of Explosive Cutting Process. Engineering Applications of Artificial Intelligence 22(4-5), 676–687 (2009)
Zanaganeh, M., Mousavi, S., Shahidi, A.: A Hybrid Genetic Algorithm–adaptive Network-based Fuzzy Inference System in Prediction of Wave Parameters. Engineering Applications of Artificial Intelligence 22(8), 1194–1202 (2009)
Dorigo, M.: Optimization, Learning and Natural Algorithms, Ph.D. Thesis. politecnico di milano, Italy (1992)
Dorigo, M., Di Caro, G.: Ant Colony Optimization: a new meta-heuristic. In: Proceeding of the 1999 Congress on Evolutionary Computation, pp. 1470–1477 (1999)
Toksarı, M.D.: Ant Colony Optimization to Estimate Energy Demand of Turkey. Energy Policy 35, 3984–3990 (2007)
Toksarı, M.: Estimating the net electricity energy generation and demand using the ant colony optimization approach: Case of Turkey. Energy Policy 37, 1181–1187 (2009)
Metropolis, N., Rosenbluth, A.W., Rosenbluth, M.N., Teller, A.H., Teller, E.: Equation of State Calculation by Fast Computing Machines. J. of Chem. Phys. 21, 1087–1091 (1953)
Kirkpatrick, S., Gelatt, C.D., Vecchi, M.P.: Optimization by Simulated Annealing. Science 220, 671–680 (1983)
Chang, P.-C., Liu, C.-H.: A TSK Type Fuzzy Rule Based System for Stock Price Prediction. Expert Systems with Applications 34, 135–144 (2008)
Jolai, F., Ghanbari, A.: Integrating Data Transformation Techniques with Hopfield Neural Networks for Solving Travelling Salesman Problem. Expert Systems with Applications solving travelling salesman problem (2010)
Azadeh, A., Asadzadeh, S., Ghanbari, A.: An Adaptive Network-based Fuzzy in Ference System Forshort-termnatural Gas Demand estimation: Uncertain and Complex environments. Energy Policy 38, 1529–1536 (2010)
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Ghanbari, A., Hadavandi, E., Abbasian-Naghneh, S. (2010). An Intelligent ACO-SA Approach for Short Term Electricity Load Prediction. In: Huang, DS., Zhang, X., Reyes García, C.A., Zhang, L. (eds) Advanced Intelligent Computing Theories and Applications. With Aspects of Artificial Intelligence. ICIC 2010. Lecture Notes in Computer Science(), vol 6216. Springer, Berlin, Heidelberg. https://doi.org/10.1007/978-3-642-14932-0_77
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DOI: https://doi.org/10.1007/978-3-642-14932-0_77
Publisher Name: Springer, Berlin, Heidelberg
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