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

Kasule, Abdal; Ayan, Kürşat

doi:10.16984/saufenbilder.629553

Cited by 7 publications

(5 citation statements)

References 26 publications

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“…The parameter optimization of the ANFIS method has been done by the PSO algorithm and genetic algorithm (GA). As a result of the study, GA-ANFIS showed the best prediction performance compared to PSO-ANFIS and MLR [14].…”

Section: Journal Of Artificial Intelligence and Systemsmentioning

confidence: 89%

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Determination of industrial energy demand in Turkey using MLR, ANFIS and PSO-ANFIS

2021

AIS

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Energy is one of the most critical inputs in social and economic development, is an essential factor in increasing living standards and creating sustainable development. Since energy is an indispensable input in all sectors, energy dependence contributed to the necessity of countries' energy policy. It has critical importance to predict energy demand to determine energy policies. According to Turkey's annual energy consumption, the industry sector has consumed the most energy in the last five years. The prediction and analysis of energy demand with economic data in the industrial sector is an essential indicator of the economic development relationship between energy demand and industry. In this study, Multiple Linear Regression (MLR), Adaptive Neuro-Fuzzy Inference System (ANFIS), and optimized ANFIS with Particle Swarm Optimization (PSO) methods are employed to forecast energy demand for Turkish industrial sectors. The indicators which affect energy consumption were determined to estimate the energy demand. The 30-year dataset between 1990 and 2019 was split as training and test set. MLR, ANFIS and PSO-ANFIS were compared according to performance evaluation, and the most proper model was identified. The coefficient of determination (R 2 ) for PSO-ANFIS, MLR, and ANFIS models are 0.9951, 0.9889, and 0.9932 in the training stage, and 0.9423, 0.9181, and 0.8776 in the testing stage, respectively. The study results indicated that the PSO-ANFIS model showed superior prediction capability with the least estimation error than MLR and ANFIS models. Consequently, parameters tuned PSO-ANFIS is able to predict the industrial energy demand in Turkey with high accuracy.

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Section: Journal Of Artificial Intelligence and Systemsmentioning

confidence: 89%

“…Kasule and Ayan [14] developed an ANFIS model to predict Uganda's electricity consumption. The parameter optimization of the ANFIS method has been done by the PSO algorithm and genetic algorithm (GA).…”

Section: Journal Of Artificial Intelligence and Systemsmentioning

confidence: 99%

Determination of industrial energy demand in Turkey using MLR, ANFIS and PSO-ANFIS

2021

AIS

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“…The ANFIS model is dependent on the Takagi-Sugeno inference system guided by the IF-THEN rule and stimulated as a five-layered neural network utilizing the FIS concept [48]. The number of membership functions and fuzzy rules play an important role in the design of ANFIS [49]. In comparison to Mamdani FIS, the Sugeno membership parameters are automatically selected [15].…”

Section: Adaptive Nero-fuzzy Inference System (Anfis)mentioning

confidence: 99%

Medical Image-Based Diagnosis Using a Hybrid Adaptive Neuro-Fuzzy Inferences System (ANFIS) Optimized by GA with a Deep Network Model for Features Extraction

Rashed,

Popescu

2024

Mathematics

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Predicting diseases in the early stages is extremely important. By taking advantage of advances in deep learning and fuzzy logic techniques, a new model is proposed in this paper for disease evaluation depending on the adaptive neuro-fuzzy inference system (ANFIS) with a genetic algorithm (GA) for classification, and the pre-trained DenseNet-201 model for feature extraction, in addition to the whale optimization algorithm (WOA) for feature selection. Two medical databases (chest X-ray and MRI brain tumor) for the diagnosis of two disease types were used as input in the suggested model. The optimization of ANFIS parameters was performed by GA to achieve the optimum prediction capability. DenseNet-201 for feature extraction was employed to obtain better classification accuracy. Having more features sometimes leads to lower accuracy, and this issue can be rectified using a feature selection strategy WOA which gave good results. The proposed model was evaluated utilizing statistical metrics root mean square error (RMSE), mean square error (MSE), standard deviation (STD), and coefficient of determination (R2), and it was compared with the conventional ANFIS model, with the proposed model (ANFIS-GA) showing a superior prediction capability over the ANFIS model. As a result, it can be concluded that the proposed ANFIS-GA model is efficient and has the potential for a robust diseases evaluation with good accuracy. Also, we conclude from this work that integrating optimization algorithms with ANFIS boosts its performance, resulting in a more accurate and reliable model.

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“…A hybrid PSO‐ANFIS approach for short‐term forecasting of consumption in Portugal was developed in [51]. Using the same PSO‐ANFIS model and the GA‐ANFIS model, [52] showed a forecast of electricity consumption in Uganda. In addition, other fairly efficient models have been developed in particular by the author in [53] who developed a novel short‐term prediction based on a genetic algorithm for adaptive neuro‐fuzzy inference system (ANFIS).…”

Section: Introductionmentioning

confidence: 99%

A hybrid model for forecasting the consumption of electrical energy in a smart grid

Souhe

Mbey

Boum

et al. 2022

The Journal of Engineering

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This paper develops a novel hybrid model for forecasting electrical consumption based on several deep learning and optimization models such as Support Vector Regression (SVR), Firefly Algorithm (FA) and Adaptive Neuro-Fuzzy Inference System (ANFIS). The process is focused on the minimization of error and risk. FA is used to optimize the forecasting performance using its higher optimization ability. The proposed SVR-FA-ANFIS model is based on historical data from smart meters in order to estimate and improve the accuracy of forecasting electrical consumption. Several accuracy coefficients such as Root Mean Square Error (RMSE), Mean Absolute Error (MAE) and Mean Absolute Percentage Error (MAPE) are used to characterize the superior performance of the proposed model. The consumption data in Cameroon over the 24-year period are used to evaluate the performance of the models. The simulation results show that the proposed method outperforms other models such as Long Short-Term Memory (LSTM) and Random Forest (RF).

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

Cited by 7 publications

References 26 publications

Determination of industrial energy demand in Turkey using MLR, ANFIS and PSO-ANFIS

Determination of industrial energy demand in Turkey using MLR, ANFIS and PSO-ANFIS

Medical Image-Based Diagnosis Using a Hybrid Adaptive Neuro-Fuzzy Inferences System (ANFIS) Optimized by GA with a Deep Network Model for Features Extraction

A hybrid model for forecasting the consumption of electrical energy in a smart grid

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