Using PSO and Genetic Algorithms to Optimize ANFIS Model for Forecasting Uganda’s Net Electricity Consumption

Yıl 2020, Cilt: 24 Sayı: 2, 324 - 337, 01.04.2020

Abdal Kasule , Kürşat Ayan

https://doi.org/10.16984/saufenbilder.629553

Cited By: 2

Öz

Uganda seeks to transform its society from a peasant to a modern and largely urban society by the year 2040. To achieve this, electricity as a form of modern and clean energy has been identified as a driving force for all the sectors of the economy. For this reason, electricity consumption forecasts that are realistic and accurate are key inputs to policy making and investment decisions for developing Uganda’s electricity sector. In this study, we present an ANFIS long-term electricity forecasting model that is easy to interpret. We use the model to forecast Uganda’s electricity consumption. The ANFIS model takes population, gross domestic product, number of subscribers and average electricity price as input variables and electricity consumption as the output. We use particle swarm optimization (PSO) algorithm and genetic algorithm (GA) to optimize the parameters of the model. A forecast accuracy of 94.34% is achieved for GA-ANFIS, while 90.88% accuracy is achieved for PSO-ANFIS as compared to 87.79% for multivariate linear regression (MLR) model. Comparison with official forecasts made by Ministry of Energy and Mineral Development (MEMD) revealed low forecast errors. 

Anahtar Kelimeler

Electricity consumption forecasting, Adaptive Neuro-Fuzzy Inference System, Genetic algorithm, Particle Swarm Optimization, Uganda

Kaynakça

• [1] Shihabudheen KV, Pillai GN, "Recent advances in neuro-fuzzy system: A survey." Knowledge-Based Syst, 152:136–162, 2018.
• [2] Mollaiy-Berneti S, "Optimal design of adaptive neuro-fuzzy inference system using genetic algorithm for electricity demand forecasting in Iranian industry". Soft Comput 20(12):4897–4906, 2016.
• [3] Pousinho HMI, Mendes VM F, Catalão JPS, "A hybrid PSO – ANFIS approach for short-term wind power prediction in Portugal." Energy Convers. Manag, 52(1):397–402, 2011.
• [4] Azadeh A, Saberi M, Gitiforouz A, Saberi Z, "A hybrid simulation-adaptive network based fuzzy inference system for improvement of electricity consumption estimation." Expert Syst. Appl., 36(8):11108–11117, 2009.
• [5] Kazemi SMR, Seied HMM, Abbasian-Naghneh S, Rahmati SHA, "An evolutionary-based adaptive neuro-fuzzy inference system for intelligent short-term load forecasting." Int. Trans. Oper. Res., 21(2):311–326, 2014.
• [6] Mamlook R, Badran O, Abdulhadi E, "A fuzzy inference model for short-term load forecasting." Energy Policy, 37:1239–1248, 2009.
• [7] Abraham A, Nath B, "A neuro-fuzzy approach for modelling electricity demand in Victoria." Appl. Soft Comput, 1:127–138, 2001.
• [8] Pereira CM, De Almeida NN, Velloso MLF, "Fuzzy modeling to forecast an electric load time series." Procedia Comput. Sci., 55:395–404, 2015.
• [9] Yang Y, Chen Y, Wang Y, Li C, Li L, "Modelling a combined method based on ANFIS and neural network improved by DE algorithm: A case study for short-term electricity demand forecasting." Appl. Soft Comput., 49:663–675, 2016.
• [10] Ucenic C, George A, "A Neuro-fuzzy Approach to Forecast the Electricity Demand." In Proceedings of the 2006 IASME/WSEAS International Conference on Energy & Environmental Systems, 299–304, 2006.
• [11] Sarduy JRG, Di Santo KG, Saidel MA, "Linear and non-linear methods for prediction of peak load at University of São Paulo." Meas. J. Int. Meas. Confed., 78:187–201, 2016.
• [12] Zahedi G, Azizi S, Bahadori A, Elkamel A, Wan SR, "Electricity demand estimation using an adaptive neuro-fuzzy network : A case study from the Ontario province-Canada." Energy, 49:323–328, 2013.
• [13] Mordjaoui B, Boudjema M, "Forecasting and Modelling Electricity Demand Using Anfis Predictor." J. Math. Stat., 7(4):275–281, 2011.
• [14] Saravanan S, Kannan S. Thangaraj C, "Prediction of India’s Electricity Consumption using ANFIS." ICTACT J. Soft Comput., 5(3):985–990, 2015.
• [15] Çevik H, Çunkaş M, (2015), Short-term load forecasting using fuzzy logic and ANFIS, Neural Comput Appl., 26:1355–1367.
• [16] Ying M, Pan L, "Using adaptive network based fuzzy inference system to forecast regional electricity loads." Energy Convers. Manag., 49:205–211, 2008.
• [17] Haydari M, Kavehnia Z, Askari F, Ganbariyan M, (2007), Time-Series Load Modelling and Load Forecasting Using Neuro-Fuzzy Techniques. In 9th International Conference on Electrical Power Quality and Utilization.
• [18] Al-Ghandoor M, Samhouri A, "Electricity Consumption in the Industrial Sector of Jordan : Application of Multivariate Linear Regression and Adaptive Neuro-Fuzzy Techniques." Jordan J. Mech. Ind. Eng., 3(1):69–76, 2009.
• [19] Jang JR, "ANFIS : Adap tive-Ne twork-Based Fuzzy Inference System." IEEE Trans. Syst. Man. Cybern., 23(3):665–685, 1993.
• [20] Takagi T, Sugeno M, "Fuzzy Identification of Systems and Its Applications to Modeling and Control." IEEE Trans. Syst. Man. Cybern., 15(1):116–132, 1985.
• [21] Del Valle RG, Venayagamoorthy Y, Mohagheghi GK, Hernandez S, Harley JC, Particle swarm optimization: basic concepts, variants and applications in power systems. IEEE Trans. Evol. Comput., 12(2):171–195.
• [22] Talpur N, Salleh MNM, Hussain K, "An investigation of membership functions on performance of ANFIS for solving classification problems." IOP Conf. Ser. Mater. Sci. Eng., 226(1) , 2017.
• [23] Rini DP, Shamsuddin SM, Yuhaniz SS, Particle swarm optimization for ANFIS interpretability and accuracy. Soft Comput., 20(1):251–262, 2016.
• [24] Kasule A, Ayan K, Forecasting Uganda’s Net Electricity Consumption Using a Hybrid PSO-ABC Algorithm, Arab J Sci Eng, 44: 3021, 2019.