Soft Computing Approach in Modeling Energy Consumption

Chiroma, Haruna; Kareem, Sameem Abdul; Sari, Eka Novita; Abdullah, Zailani; Muaz, Sanah Abdullahi; Kaynar, Oğuz; Shah, Habib; Herawan, Tutut

doi:10.1007/978-3-319-09153-2_57

Cited by 6 publications

(2 citation statements)

References 21 publications

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“…In Chiroma et al . [ 64 ], their proposed meta-heuristic algorithm method of modelling oil consumption was compared with other meta-heuristic algorithms. Tables 4 – 7 summarised the simulation results; the first column is the data partition ratio, whereas the second, third, and fourth columns are the mean, best, and worst results, respectively.…”

Section: Resultsmentioning

confidence: 99%

Global Warming: Predicting OPEC Carbon Dioxide Emissions from Petroleum Consumption Using Neural Network and Hybrid Cuckoo Search Algorithm

et al. 2015

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BackgroundGlobal warming is attracting attention from policy makers due to its impacts such as floods, extreme weather, increases in temperature by 0.7°C, heat waves, storms, etc. These disasters result in loss of human life and billions of dollars in property. Global warming is believed to be caused by the emissions of greenhouse gases due to human activities including the emissions of carbon dioxide (CO2) from petroleum consumption. Limitations of the previous methods of predicting CO2 emissions and lack of work on the prediction of the Organization of the Petroleum Exporting Countries (OPEC) CO2 emissions from petroleum consumption have motivated this research.Methods/FindingsThe OPEC CO2 emissions data were collected from the Energy Information Administration. Artificial Neural Network (ANN) adaptability and performance motivated its choice for this study. To improve effectiveness of the ANN, the cuckoo search algorithm was hybridised with accelerated particle swarm optimisation for training the ANN to build a model for the prediction of OPEC CO2 emissions. The proposed model predicts OPEC CO2 emissions for 3, 6, 9, 12 and 16 years with an improved accuracy and speed over the state-of-the-art methods.ConclusionAn accurate prediction of OPEC CO2 emissions can serve as a reference point for propagating the reorganisation of economic development in OPEC member countries with the view of reducing CO2 emissions to Kyoto benchmarks—hence, reducing global warming. The policy implications are discussed in the paper.

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Section: Resultsmentioning

confidence: 99%

Global Warming: Predicting OPEC Carbon Dioxide Emissions from Petroleum Consumption Using Neural Network and Hybrid Cuckoo Search Algorithm

et al. 2015

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“…The operators employed in a simple GA include selection, crossover, and mutation [46]. GAs are often regarded as function optimizers and they have been applied in many optimization problems such as energy consumption [48]. In particular, the use of GAs for fuzzy systems design equips them with the adaptation and learning capabilities which bring about genetic fuzzy systems [10].…”

Section: Optimization Methodsmentioning

confidence: 99%

Genetic Algorithm and Particle Swarm Optimization Based Cascade Interval Type 2 Fuzzy PD Controller for Rotary Inverted Pendulum System

Hamza

Yap

Choudhury

2015

Mathematical Problems in Engineering

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This paper presents the design of an optimized Interval Type 2 Fuzzy Proportional Derivative Controller (IT2F-PDC) in cascade form for Rotary Inverted Pendulum (RIP) system. The parameters of the IT2F-PDC are optimised by using Genetic Algorithm (GA) and Particle Swarm Optimization (PSO). The goal is to balance the pendulum in upright unstable equilibrium position. The IT2F-PDC which is the extended version of conventional type 1 fuzzy logic controller, improves the control strategy by using the advantage of its footprint of uncertainty for the fuzzy membership function. The performance characteristics considered for the controller are steady state error, settling time, rise time, maximum overshoot, and control energy. Experimental and simulation results indicated that the effectiveness and robustness of the proposed GA- and PSO-based controllers on the RIP with respect to load disturbances, parameter variation, and noise effects have been improved over state-of-the-art method. However, the comparative results for simulation and experiment based on cascade IT2F-PDC indicate that GA-based IT2F-PDC has lower steady state error while PSO-based IT2F-PDC has lower overshoot, settling time, and control energy, but both have almost the same rise time. The proposed control strategy can be regarded as a promising strategy for controlling different unstable and nonlinear systems.

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