Hybrid neurofuzzy wind power forecast and wind turbine location for embedded generation
- Adedeji, Paul A., Akinlabi, Stephen A., Madushele, Nkosinathi, Olatunji, Obafemi O.
- Authors: Adedeji, Paul A. , Akinlabi, Stephen A. , Madushele, Nkosinathi , Olatunji, Obafemi O.
- Date: 2020
- Subjects: ANFIS , Embedded generation genetic algorithm , Genetic algorithm
- Language: English
- Type: Article
- Identifier: http://hdl.handle.net/10210/436546 , uj:37871
- Description: Abstract: , Wind energy uptake in South Africa is significantly increasing both at the micro‐ and macro‐level and the possibility of embedded generation cannot be undermined considering the state of electricity supply in the country. This study identifies a wind hotspot site in the Eastern Cape province, performs an in silico deployment of three utility‐scale wind turbines of 60 m hub height each from different manufacturers, develops machine learning models to forecast very short‐term power production of the three wind turbine generators (WTG) and investigates the feasibility of embedded generation for a potential livestock industry in the area. Windographer software was used to characterize and simulate the net output power from these turbines using the wind speed of the potential site. Two hybrid models of adaptive neurofuzzy inference system (ANFIS) comprising genetic algorithm and particle swarm optimization (PSO) each for a turbine were developed to forecast very short‐term power output. The feasibility of embedded generation for typical medium‐scale agricultural industry was investigated using a weighted Weber facility location model. The analytical hierarchical process (AHP) was used for weight determination. From our findings, the WTG‐1 was selected based on its error performance metrics (root mean square error of 0.180, mean absolute SD of 0.091 and coefficient of determination of 0.914 and CT = 702.3 seconds) in the optimal model (PSO‐ANFIS). Criteria were ranked based on their order of significance to the agricultural industry as proximity to water supply, labour availability, power supply and road network. Also, as a proof of concept, the optimal location of the industrial facility relative to other criteria was X = 19.24 m, Y = 47.11 m. This study reveals the significance of resource forecasting and feasibility of embedded generation, thus improving the quality of preliminary resource assessment and facility location among site developers.
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- Authors: Adedeji, Paul A. , Akinlabi, Stephen A. , Madushele, Nkosinathi , Olatunji, Obafemi O.
- Date: 2020
- Subjects: ANFIS , Embedded generation genetic algorithm , Genetic algorithm
- Language: English
- Type: Article
- Identifier: http://hdl.handle.net/10210/436546 , uj:37871
- Description: Abstract: , Wind energy uptake in South Africa is significantly increasing both at the micro‐ and macro‐level and the possibility of embedded generation cannot be undermined considering the state of electricity supply in the country. This study identifies a wind hotspot site in the Eastern Cape province, performs an in silico deployment of three utility‐scale wind turbines of 60 m hub height each from different manufacturers, develops machine learning models to forecast very short‐term power production of the three wind turbine generators (WTG) and investigates the feasibility of embedded generation for a potential livestock industry in the area. Windographer software was used to characterize and simulate the net output power from these turbines using the wind speed of the potential site. Two hybrid models of adaptive neurofuzzy inference system (ANFIS) comprising genetic algorithm and particle swarm optimization (PSO) each for a turbine were developed to forecast very short‐term power output. The feasibility of embedded generation for typical medium‐scale agricultural industry was investigated using a weighted Weber facility location model. The analytical hierarchical process (AHP) was used for weight determination. From our findings, the WTG‐1 was selected based on its error performance metrics (root mean square error of 0.180, mean absolute SD of 0.091 and coefficient of determination of 0.914 and CT = 702.3 seconds) in the optimal model (PSO‐ANFIS). Criteria were ranked based on their order of significance to the agricultural industry as proximity to water supply, labour availability, power supply and road network. Also, as a proof of concept, the optimal location of the industrial facility relative to other criteria was X = 19.24 m, Y = 47.11 m. This study reveals the significance of resource forecasting and feasibility of embedded generation, thus improving the quality of preliminary resource assessment and facility location among site developers.
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Sustainable supplier selection in a paint manufacturing company using hybrid meta-heuristic algorithm
- Machesa, M. G. K., Tartibu, L. K., Okwu, M. O.
- Authors: Machesa, M. G. K. , Tartibu, L. K. , Okwu, M. O.
- Date: 2020
- Subjects: Supplier selection , Hybrid Algorithm , ANFIS
- Language: English
- Type: Article
- Identifier: http://hdl.handle.net/10210/460016 , uj:40919 , Citation: Machesa, M.G.K., Tartibu, L.K. & Okwu, M.O. 2020. Sustainable supplier selection in a paint manufacturing company using hybrid meta-heuristic algorithm.
- Description: Abstract: Supplier selection in a manufacturing system is highly complex due to the stochastic nature and structure of organizations, thereby necessitating a paradigm shift from the rule of thumb and classical methods of supplier selection to a reliable technique, using the hybrid algorithm to provide higher accuracy in the selection process. Hence, this study proposes the use of hybrid computational intelligence technique, Adaptive Neuro-Fuzzy Inference Systems (ANFIS) for effective prediction and sustainable selection of suppliers (SSS). This hybrid modelling configuration was applied in a paint manufacturing company to select the best possible supplier. Information obtained from the company within the period of investigation was fed into the model. The result obtained shows a faster and reliable prediction of the creative model. Professionals and business managers will benefit greatly from SSS in an in-bound and out-bound supply chain system.
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- Authors: Machesa, M. G. K. , Tartibu, L. K. , Okwu, M. O.
- Date: 2020
- Subjects: Supplier selection , Hybrid Algorithm , ANFIS
- Language: English
- Type: Article
- Identifier: http://hdl.handle.net/10210/460016 , uj:40919 , Citation: Machesa, M.G.K., Tartibu, L.K. & Okwu, M.O. 2020. Sustainable supplier selection in a paint manufacturing company using hybrid meta-heuristic algorithm.
- Description: Abstract: Supplier selection in a manufacturing system is highly complex due to the stochastic nature and structure of organizations, thereby necessitating a paradigm shift from the rule of thumb and classical methods of supplier selection to a reliable technique, using the hybrid algorithm to provide higher accuracy in the selection process. Hence, this study proposes the use of hybrid computational intelligence technique, Adaptive Neuro-Fuzzy Inference Systems (ANFIS) for effective prediction and sustainable selection of suppliers (SSS). This hybrid modelling configuration was applied in a paint manufacturing company to select the best possible supplier. Information obtained from the company within the period of investigation was fed into the model. The result obtained shows a faster and reliable prediction of the creative model. Professionals and business managers will benefit greatly from SSS in an in-bound and out-bound supply chain system.
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Wind turbine power output short-term forecast : a comparative study of data clustering techniques in a PSO-ANFIS model
- Adedeji, Paul A., Akinlabi, Stephen, Madushele, Nkosinathi, Olatunji, Obafemi O.
- Authors: Adedeji, Paul A. , Akinlabi, Stephen , Madushele, Nkosinathi , Olatunji, Obafemi O.
- Date: 2020
- Subjects: ANFIS , Autoregressive model , Data clustering
- Language: English
- Type: Article
- Identifier: http://hdl.handle.net/10210/436554 , uj:37872
- Description: Abstract: , The emergence of new sites for wind energy exploration in South Africa requires an accurate prediction of the potential power output of a typical utility-scale wind turbine in such areas. However, careful selection of data clustering technique is very essential as it has a significant impact on the accuracy of the prediction. Adaptive neurofuzzy inference system (ANFIS), both in its standalone and hybrid form has been applied in offline and online forecast in wind energy studies, however, the effect of clustering techniques has not been reported despite its significance. Therefore, this study investigates the effect of the choice of clustering algorithm on the performance of a standalone ANFIS and ANFIS optimized with particle swarm optimization (PSO) technique using a synthetic wind turbine power output data of a potential site in the Eastern Cape, South Africa. In this study a wind resource map for the Eastern Cape province was developed. Also, autoregressive ANFIS models and their hybrids with PSO were developed. Each model was evaluated based on three clustering techniques (grid partitioning (GP), subtractive clustering (SC), and fuzzy-c-means (FCM)). The gross wind power of the model wind turbine was estimated from the wind speed data collected from the potential site at 10 min data resolution using Windographer software. The standalone and hybrid models were trained and tested with 70% and 30% of the dataset respectively. The performance of each clustering technique was compared for both standalone and PSO-ANFIS models using known statistical metrics. From our findings, ANFIS standalone model clustered with SC performed best among the standalone models with a root mean square error (RMSE) of 0.132, mean absolute percentage error (MAPE) of 30.94, a mean absolute deviation (MAD) of 0.077, relative mean bias error (rMBE) of 0.190 and variance accounted for (VAF) of 94.307. Also, PSO-ANFIS model clustered with SC technique performed the best among the three hybrid models with RMSE of 0.127, MAPE of 28.11, MAD of 0.078, rMBE of 0.190 and VAF of 94.311. The ANFIS-SC model recorded the lowest computational time of 30.23secs among the standalone models. However, the PSO-ANFIS-SC model recorded a computational time of 47.21secs. Based on our findings, a hybrid ANFIS model gives better forecast accuracy compared to the standalone model, though with a trade-off in the computational time. Since, the choice of clustering technique was observed to play a vital role in the forecast accuracy of standalone and hybrid models, this study recommends SC technique for ANFIS modeling at both standalone and hybrid models.
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- Authors: Adedeji, Paul A. , Akinlabi, Stephen , Madushele, Nkosinathi , Olatunji, Obafemi O.
- Date: 2020
- Subjects: ANFIS , Autoregressive model , Data clustering
- Language: English
- Type: Article
- Identifier: http://hdl.handle.net/10210/436554 , uj:37872
- Description: Abstract: , The emergence of new sites for wind energy exploration in South Africa requires an accurate prediction of the potential power output of a typical utility-scale wind turbine in such areas. However, careful selection of data clustering technique is very essential as it has a significant impact on the accuracy of the prediction. Adaptive neurofuzzy inference system (ANFIS), both in its standalone and hybrid form has been applied in offline and online forecast in wind energy studies, however, the effect of clustering techniques has not been reported despite its significance. Therefore, this study investigates the effect of the choice of clustering algorithm on the performance of a standalone ANFIS and ANFIS optimized with particle swarm optimization (PSO) technique using a synthetic wind turbine power output data of a potential site in the Eastern Cape, South Africa. In this study a wind resource map for the Eastern Cape province was developed. Also, autoregressive ANFIS models and their hybrids with PSO were developed. Each model was evaluated based on three clustering techniques (grid partitioning (GP), subtractive clustering (SC), and fuzzy-c-means (FCM)). The gross wind power of the model wind turbine was estimated from the wind speed data collected from the potential site at 10 min data resolution using Windographer software. The standalone and hybrid models were trained and tested with 70% and 30% of the dataset respectively. The performance of each clustering technique was compared for both standalone and PSO-ANFIS models using known statistical metrics. From our findings, ANFIS standalone model clustered with SC performed best among the standalone models with a root mean square error (RMSE) of 0.132, mean absolute percentage error (MAPE) of 30.94, a mean absolute deviation (MAD) of 0.077, relative mean bias error (rMBE) of 0.190 and variance accounted for (VAF) of 94.307. Also, PSO-ANFIS model clustered with SC technique performed the best among the three hybrid models with RMSE of 0.127, MAPE of 28.11, MAD of 0.078, rMBE of 0.190 and VAF of 94.311. The ANFIS-SC model recorded the lowest computational time of 30.23secs among the standalone models. However, the PSO-ANFIS-SC model recorded a computational time of 47.21secs. Based on our findings, a hybrid ANFIS model gives better forecast accuracy compared to the standalone model, though with a trade-off in the computational time. Since, the choice of clustering technique was observed to play a vital role in the forecast accuracy of standalone and hybrid models, this study recommends SC technique for ANFIS modeling at both standalone and hybrid models.
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Neuro-fuzzy mid-term forecasting of electricity consumption using meteorological data
- Adedeji, Paul A, Akinlabi, Stephen, Madushele, Nkosinathi, Olatunji, Obafemi
- Authors: Adedeji, Paul A , Akinlabi, Stephen , Madushele, Nkosinathi , Olatunji, Obafemi
- Date: 2019
- Subjects: ANFIS , Electricity Consumption , FCM
- Language: English
- Type: Conference proceedings
- Identifier: http://hdl.handle.net/10210/406893 , uj:34228 , Citation: Adedeji, P.A. et al. 2019 : Neuro-fuzzy mid-term forecasting of electricity consumption using meteorological data.
- Description: Abstract : Forecasting energy consumption is highly essential for strategic and operational planning. This study uses the Adaptive-Neuro-Fuzzy Inference System (ANFIS) for a mid-term forecast of electricity consumption. The model comprises of three meteorological variables as inputs and electricity consumption as output. Two ANFIS models with two clustering techniques (Fuzzy c-Means (FCM) and Grid Partitioning (GP) were developed (ANFIS-FCM and ANFIS- GP) to forecast monthly energy consumption based on meteorological variables. The performance of each model was determined using known statistical metrics. This compares the predicted electricity consumption with the observed and a statistical significance between the two reported. ANFIS-FCM model recorded a better mean absolute deviation (MAD), root mean square (RMSE), and mean absolute percentage error (MAPE) values of 0.396, 0.738, and 8.613 respectively compared to the ANFIS-GP model, which has MAD, RMSE, and MAPE values of 0.450, 0.762, and 9.430 values respectively. The study established that FCM is a good clustering technique in ANFIS compared to GP and recommended a comparison between the two techniques on hybrid ANFIS model.
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- Authors: Adedeji, Paul A , Akinlabi, Stephen , Madushele, Nkosinathi , Olatunji, Obafemi
- Date: 2019
- Subjects: ANFIS , Electricity Consumption , FCM
- Language: English
- Type: Conference proceedings
- Identifier: http://hdl.handle.net/10210/406893 , uj:34228 , Citation: Adedeji, P.A. et al. 2019 : Neuro-fuzzy mid-term forecasting of electricity consumption using meteorological data.
- Description: Abstract : Forecasting energy consumption is highly essential for strategic and operational planning. This study uses the Adaptive-Neuro-Fuzzy Inference System (ANFIS) for a mid-term forecast of electricity consumption. The model comprises of three meteorological variables as inputs and electricity consumption as output. Two ANFIS models with two clustering techniques (Fuzzy c-Means (FCM) and Grid Partitioning (GP) were developed (ANFIS-FCM and ANFIS- GP) to forecast monthly energy consumption based on meteorological variables. The performance of each model was determined using known statistical metrics. This compares the predicted electricity consumption with the observed and a statistical significance between the two reported. ANFIS-FCM model recorded a better mean absolute deviation (MAD), root mean square (RMSE), and mean absolute percentage error (MAPE) values of 0.396, 0.738, and 8.613 respectively compared to the ANFIS-GP model, which has MAD, RMSE, and MAPE values of 0.450, 0.762, and 9.430 values respectively. The study established that FCM is a good clustering technique in ANFIS compared to GP and recommended a comparison between the two techniques on hybrid ANFIS model.
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Use of MPPT techniques to reduce the energy pay-back time in PV systems
- Farayola, Adedayo M., Hasan, Ali N., Ali, Ahmed
- Authors: Farayola, Adedayo M. , Hasan, Ali N. , Ali, Ahmed
- Date: 2018
- Subjects: Artificial Intelligence (AI) , ANFIS , ANN
- Language: English
- Type: Conference proceedings
- Identifier: http://hdl.handle.net/10210/274581 , uj:29302 , Citation: Farayola, A.M., Hasan, A.N. & Ali, A. 2018. Use of MPPT techniques to reduce the energy pay-back time in PV systems.
- Description: Abstract: Photovoltaic (PV) energy is a free-energy that is used as an alternative to fossil fuel energy. However, PV system without maximum power point tracking (MPPT) produces a low, unstable power and with a long energy pay-back time. This paper presents an innovative artificial neuro-fuzzy inference system (ANFIS) MPPT technique that could extract maximum power from a complete PV system and with a lessened EPBT. To confirm the effectiveness of the ANFIS algorithm, its result was compared with the results of PV system using Perturb&Observe (P&O) technique, non-MPPT technique, combination of artificial neural network and support vector machine as ANN-SVM technique and using Pretoria city weather data as case studies. Results show that ANFIS-MPPT yielded the best result and with the lowest EPBT.
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- Authors: Farayola, Adedayo M. , Hasan, Ali N. , Ali, Ahmed
- Date: 2018
- Subjects: Artificial Intelligence (AI) , ANFIS , ANN
- Language: English
- Type: Conference proceedings
- Identifier: http://hdl.handle.net/10210/274581 , uj:29302 , Citation: Farayola, A.M., Hasan, A.N. & Ali, A. 2018. Use of MPPT techniques to reduce the energy pay-back time in PV systems.
- Description: Abstract: Photovoltaic (PV) energy is a free-energy that is used as an alternative to fossil fuel energy. However, PV system without maximum power point tracking (MPPT) produces a low, unstable power and with a long energy pay-back time. This paper presents an innovative artificial neuro-fuzzy inference system (ANFIS) MPPT technique that could extract maximum power from a complete PV system and with a lessened EPBT. To confirm the effectiveness of the ANFIS algorithm, its result was compared with the results of PV system using Perturb&Observe (P&O) technique, non-MPPT technique, combination of artificial neural network and support vector machine as ANN-SVM technique and using Pretoria city weather data as case studies. Results show that ANFIS-MPPT yielded the best result and with the lowest EPBT.
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Curve fitting polynomial technique compared to ANFIS technique for maximum power point tracking
- Farayola, Adedayo M., Hasan, Ali N., Ali, Ahmad
- Authors: Farayola, Adedayo M. , Hasan, Ali N. , Ali, Ahmad
- Date: 2017
- Subjects: ANFIS , Artificial Intelligence (AI) , Curve fitting polynomials
- Language: English
- Type: Conference proceedings
- Identifier: http://hdl.handle.net/10210/232998 , uj:23778 , Citation: Farayola, A.M., Hasan, A.N. & Ali, A. 2017. Curve fitting polynomial technique compared to ANFIS technique for maximum power point tracking. The 8th International Renewable Energy Congress (IREC 2017).
- Description: Abstract: In this paper, an approach of designing a fast tracking MPPT is introduced using a predicted sixth order polynomial curve fitting MPPT technique. The results are compared with the lower order polynomials curve fitting MPPT and also compared with the Artificial Neuro-Fuzzy Inference System (ANFIS) results. The polynomials were generated from an offline solar data. This work was done to validate the effect of using a higher order polynomials under various weather conditions using modified CUK DC-DC converter. Findings suggest that using the 6th order polynomial curve fitting and the ANFIS techniques could track the highest maximum power point than the lower order curve techniques.
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- Authors: Farayola, Adedayo M. , Hasan, Ali N. , Ali, Ahmad
- Date: 2017
- Subjects: ANFIS , Artificial Intelligence (AI) , Curve fitting polynomials
- Language: English
- Type: Conference proceedings
- Identifier: http://hdl.handle.net/10210/232998 , uj:23778 , Citation: Farayola, A.M., Hasan, A.N. & Ali, A. 2017. Curve fitting polynomial technique compared to ANFIS technique for maximum power point tracking. The 8th International Renewable Energy Congress (IREC 2017).
- Description: Abstract: In this paper, an approach of designing a fast tracking MPPT is introduced using a predicted sixth order polynomial curve fitting MPPT technique. The results are compared with the lower order polynomials curve fitting MPPT and also compared with the Artificial Neuro-Fuzzy Inference System (ANFIS) results. The polynomials were generated from an offline solar data. This work was done to validate the effect of using a higher order polynomials under various weather conditions using modified CUK DC-DC converter. Findings suggest that using the 6th order polynomial curve fitting and the ANFIS techniques could track the highest maximum power point than the lower order curve techniques.
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