Long-term electrical energy consumption formulating and forecasting via optimized gene expression programming

• Published in: Energy (Oxford) Vol. 126; pp. 144 - 164
• Main Authors: Kaboli, S. Hr. Aghay, Fallahpour, A., Selvaraj, J., Rahim, N.A.
• Format: Journal Article
• Language: English
• Published: Oxford Elsevier Ltd 01.05.2017; Elsevier BV
• Subjects: Accuracy; Adaptive systems; Algorithms; Artificial intelligence; Artificial neural networks; Computer simulation; Cuculidae; data collection; Data mining; Data processing; electric energy consumption; electric power; Electrical energy consumption; Electricity consumption; Energy consumption; Exact solutions; Forecasting; Fuzzy logic; Fuzzy systems; Gene expression; Gene expression programming; Learning theory; Machine learning; Mathematical models; Neural networks; Optimization; Particle swarm optimization; probability; Robustness (mathematics); Search algorithms; Support vector machines; Swarm intelligence; Electrical energy consumption; Forecasting; Optimization; Gene expression programming
• ISSN: 0360-5442; 1873-6785
• DOI: 10.1016/j.energy.2017.03.009

This study formulates the effects of two different historical data types on electrical energy consumption of ASEAN-5 counties. On this basis, optimized GEP (gene expression programming) is applied to precisely formulate the relationships between historical data and electricity consumption. The optimized GEP is a more recent extension of GEP with high probability of finding closed-form solution in mathematical modeling without prior knowledge about the nature of the relationships between variables. This merit is provided by balancing the exploration of solution structure and exploitation of its appropriate weighting factors through use of a robust and efficient optimization algorithm in learning process of GEP. To assess the applicability and accuracy of the proposed method, its estimates are compared with those obtained from ANN (artificial neural network), SVR (support vector regression), ANFIS (adaptive neuro-fuzzy inference system), rule-based data mining algorithm, GEP, linear and quadratic models optimized by PSO (particle swarm optimization), CSA (cuckoo search algorithm) and BSA (backtracking search algorithm). The simulation results are validated by actual data sets observed from 1971 until 2011. The results confirm the higher accuracy of the proposed method as compared with other artificial intelligence based models. Future estimations of electrical energy consumption in ASEAN-5 countries are projected up to 2030 according to rolling-based forecasting procedure. •The electrical energy consumption in ASEAN-5 countries is precisely formulated.•The most effective input historical data type is determined by parallel comparison.•The accuracy of the proposed method is compared with other methodologies.•Future electrical energy consumption in ASEAN-5 countries is projected up to 2030.