Emerging Harris Hawks Optimization based load demand forecasting and optimal sizing of stand-alone hybrid renewable energy systems– A case study of Kano and Abuja, Nigeria

Abba, S.I.; Najashi, Bara'u Gafai; Rotimi, Abdulazeez; Musa, Bashir; Yimen, Nasser; Kawu, Sagir Jibrin; Lawan, S. M.; Dagbasi, Mustafa

doi:10.1016/j.rineng.2021.100260

Cited by 42 publications

(19 citation statements)

References 33 publications

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“…They also considered the unpredictability of Wind speed and clearness index. Ahmed Elnozahy [25] proposed weather unpredictability, a probabilistic technique based on an artificial neural network is used. A complete eco-techno-economic optimization research is integrated with the established choices and strategy.…”

Section: Related Workmentioning

confidence: 99%

“…The data is classified into nodes and the node relationships can be formulated through an algorithm [24]. The percentage deviation for the MILP can be formulated as follows [25].…”

Section: Dataset Generation Using Mixed Interger Linear Programming (...mentioning

confidence: 99%

“…The use of MILP has also been used for the scheduling of tasks. Scheduling is a complicated task that is mainly theoretical and works for various real-world systems [25]. The MILP is applicable for the minimization of the costs associated with the handling of goods.…”

Section: Dataset Generation Using Mixed Interger Linear Programming (...mentioning

confidence: 99%

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Analyzing Optimal Battery Sizing in Microgrids Based on the Feature Selection and Machine Learning Approaches

Khan¹,

Nizami²,

Qaisar³

et al. 2022

Preprint

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Microgrids are becoming popular nowadays because they provide clean, efficient, and low-cost energy. To use the stored energy in times of emergency or peak loads, microgrids require bulk storage capacity. Since microgrids are the future of renewable energy, the energy storage technology employed should be optimized to generate electricity. Batteries play a variety of essential roles in daily life and are used at peak hours and during a time of emergency. There are different types of batteries i.e., lion batteries, lead-acid batteries, etc. Optimal battery sizing of microgrids is a challenging problem, that limits modern technologies such as electric vehicles, etc. It is important to know different battery features such as battery life, battery throughput, and battery autonomy to get optimal battery sizing for microgrids. Mixed-integer linear programming (MILP) is an established technique for the integration and optimization of different energy sources and parameters for optimal battery sizing. A new MILP based dataset is introduced in this work. Support vector machine (SVM) is the machine learning application used to estimate the optimum battery size. The impact of feature selection algorithms on the proposed machine learning-based model is evaluated. The performance of the six best-performing feature selection algorithms is analyzed. The experimental results show that the feature selection algorithms improve the performance of the proposed methodology. Ranker search shows the best performance with a Spearman’s rank-ordered correlation constant of 0.9756, linear correlation constant of 0.9452, Kendall correlation constant of 0.8488 and root mean squared error of 0.0525.

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Section: Related Workmentioning

confidence: 99%

“…The data is classified into nodes and the node relationships can be formulated through an algorithm [24]. The percentage deviation for the MILP can be formulated as follows [25].…”

Section: Dataset Generation Using Mixed Interger Linear Programming (...mentioning

confidence: 99%

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Analyzing Optimal Battery Sizing in Microgrids Based on the Feature Selection and Machine Learning Approaches

Khan¹,

Nizami²,

Qaisar³

et al. 2022

Preprint

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show abstract

“…Both models were applied for the problem of features selection, and their analysis showed great improvements in tackling feature selection challenges. Very recent hybrid studies which integrate HHO with other metaheuristic optimization algorithms were discussed by Abba et al [ 27 ], Ebrahim et al [ 28 ], Bandyopadhyay et al [ 29 ], Suresh et al [ 30 ], and Mossa et al [ 31 ]. In [ 27 ], the hybrid of PSO with HHO for renewable energy load demand forecasting was presented.…”

Section: Introductionmentioning

confidence: 99%

“…Very recent hybrid studies which integrate HHO with other metaheuristic optimization algorithms were discussed by Abba et al [ 27 ], Ebrahim et al [ 28 ], Bandyopadhyay et al [ 29 ], Suresh et al [ 30 ], and Mossa et al [ 31 ]. In [ 27 ], the hybrid of PSO with HHO for renewable energy load demand forecasting was presented. The synergy of sine–cosine with HHO was discussed in [ 28 ] for optimizing the fuel cell−based electric power system.…”

Section: Introductionmentioning

confidence: 99%

Rules embedded harris hawks optimizer for large-scale optimization problems

Samma

Sama

2022

Neural Comput & Applic

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Harris Hawks Optimizer (HHO) is a recent optimizer that was successfully applied for various real-world problems. However, working under large-scale problems requires an efficient exploration/exploitation balancing scheme that helps HHO to escape from possible local optima stagnation. To achieve this objective and boost the search efficiency of HHO, this study develops embedded rules used to make adaptive switching between exploration/exploitation based on search performances. These embedded rules were formulated based on several parameters such as population status, success rate, and the number of consumed search iterations. To verify the effectiveness of these embedded rules in improving HHO performances, a total of six standard high-dimensional functions ranging from 1000-D to 10,000-D and CEC’2010 large-scale benchmark were employed in this study. In addition, the proposed Rules Embedded Harris Hawks Optimizer (REHHO) applied for one real-world high dimensional wavelength selection problem. Conducted experiments showed that these embedded rules significantly improve HHO in terms of accuracy and convergence curve. In particular, REHHO was able to achieve superior performances against HHO in all conducted benchmark problems. Besides that, results showed that faster convergence was obtained from the embedded rules. Furthermore, REHHO was able to outperform several recent and state-of-the-art optimization algorithms.

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Assessing the viability of hybrid renewable energy systems in Nigeria

Mas'ud,

Yunusa‐Kaltungo,

Rufa'I

et al. 2024

Engineering Reports

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The implementation of renewable energy strategies has been on the rise due to recent global initiatives on sustainable development. In this work, meteorological data obtained from geographically separated stations in Nigeria is utilized to assess the economic benefits of off‐grid renewable energy projects specifically WT/battery, PV/battery, and PV/WT/battery systems for upcoming projects in six different sites, with anticipated start dates in 2023, 2026, and 2029. By utilizing future forecasts of capital costs, this analysis aims to facilitate the decision‐making process regarding the optimal timing and placement of these installations. The WT/battery systems have a levelised cost of electricity (LCOE) that is 20–35 times more in comparison to both PV/WT/battery and PV/battery systems. In 2023, the LCOE for photovoltaic (PV)/battery systems varies from $0.16/kWh in Yola to $0.169/kWh in Port Harcourt. On the other hand, PV/WT/battery systems have the lowest LCOE, ranging from $0.261/kWh in Jos to $0.319/kWh in Port Harcourt. The analysis additionally demonstrated that in the event of project delays, PV/battery systems are projected to exhibit the lowest LCOE across all locations by 2029. This is attributed to the anticipated accelerated reduction in PV and battery expenses compared to the decline in costs associated with WTs. In the specified year, Yola exhibited the most favorable LCOE at $0.0748 per kilowatt‐hour, while Port Harcourt recorded the highest LCOE at $0.113 per kilowatt‐hour. It has been discovered that solar energy exhibits more cost‐effectiveness compared to wind energy, mostly attributable to its superior ability to match generation with load on an hourly and monthly basis, particularly in the presence of heavy loads. This study provides a comprehensive geographical overview that will assist policymakers in the strategic selection of cities in Nigeria for the deployment of off‐grid renewable energy (RE) systems.

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Emerging Harris Hawks Optimization based load demand forecasting and optimal sizing of stand-alone hybrid renewable energy systems– A case study of Kano and Abuja, Nigeria

Cited by 42 publications

References 33 publications

Analyzing Optimal Battery Sizing in Microgrids Based on the Feature Selection and Machine Learning Approaches

Analyzing Optimal Battery Sizing in Microgrids Based on the Feature Selection and Machine Learning Approaches

Rules embedded harris hawks optimizer for large-scale optimization problems

Assessing the viability of hybrid renewable energy systems in Nigeria

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