Economic Dispatch Optimization of Multi-Water Resources: A Case Study of an Island in South Korea

Gnawali, Kapil; Han, Kuk-Heon; Geem, Zong Woo; Jun, Kyung Soo; Yum, Kyung Taek

doi:10.3390/su11215964

Cited by 12 publications

(8 citation statements)

References 30 publications

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“…The use of artificial intelligence in a variety of engineering problems is increasing. As an example, the fuzzy set theory and its application [22,23], meta-heuristic algorithms [24][25][26][27][28][29][30][31], hybrid algorithms [32][33][34][35], pattern recognition, and machine learning [36][37][38][39][40] have been adopted. The GMDH type of neural network as a subset of artificial intelligence was proposed by Ivakhnenko [41], and it has a suitable capability for the training of network statistics.…”

Section: Group Methods Of Data Handling (Gmdh)mentioning

confidence: 99%

Development of a Binary Model for Evaluating Water Distribution Systems by a Pressure Driven Analysis (PDA) Approach

Morosini

Haghshenas

et al. 2020

Applied Sciences

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Investigation of Water Distribution Networks (WDNs) is considered a challenging task due to the unpredicted and uncertain conditions in water engineering. When in a WDN, a pipe failure occurs, and shut-off valves to isolate the broken pipe to allow repairing works are activated. In these new conditions, the hydraulic parameters in the network are modified because the topology of the entire system changes. If the head becomes inadequate, the Pressure Driven Analysis (PDA) is the correct approach to evaluate the performance of water networks. Hence, in the present study, the water distribution system was evaluated in pressure-driven conditions for 100 different scenarios and then using a type of neural network called Group Method of Data Handling (GMDH) as a stochastic technique. For this purpose, several most notable parameters including the base demand, pressure, and alpha (the percentage of effective supplied flow) were calculated using simulations based on a PDA approach and applied to the water distribution network of Praia a Mare in Southern Italy. In the second stage, the output parameters were used in a developed binary classification model. Finally, the obtained results showed that the GMDH algorithm can be applied as a powerful tool for modeling water distribution networks.

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Section: Group Methods Of Data Handling (Gmdh)mentioning

confidence: 99%

Development of a Binary Model for Evaluating Water Distribution Systems by a Pressure Driven Analysis (PDA) Approach

Morosini

Haghshenas

et al. 2020

Applied Sciences

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“…The HS algorithm is an efficient algorithm in unpredicted and uncertain systems that can provide a higher performance capacity for dealing with linear and nonlinear data sets. In addition, the HS algorithm has been successfully applied in the engineering and academic sectors [51][52][53][54][55][56][57]. The working principles of optimization of HS algorithm are similar to the composition of songs by musicians using various notes.…”

Section: The Harmony Search (Hs) Algorithmmentioning

confidence: 99%

Application of Harmony Search Algorithm to Slope Stability Analysis

Haghshenas

Geem

et al. 2021

Land

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Slope stability analysis is undoubtedly one of the most complex problems in geotechnical engineering and its study plays a paramount role in mitigating the risk associated with the occurrence of a landslide. This problem is commonly tackled by using limit equilibrium methods or advanced numerical techniques to assess the slope safety factor or, sometimes, even the displacement field of the slope. In this study, as an alternative approach, an attempt to assess the stability condition of homogeneous slopes was made using a machine learning (ML) technique. Specifically, a meta-heuristic algorithm (Harmony Search (HS) algorithm) and K-means algorithm were employed to perform a clustering analysis by considering two different classes, depending on whether a slope was unstable or stable. To achieve the purpose of this study, a database made up of 19 case studies with 6 model inputs including unit weight, intercept cohesion, angle of shearing resistance, slope angle, slope height and pore pressure ratio and one output (i.e., the slope safety factor) was established. Referring to this database, 17 out of 19 slopes were categorized correctly. Moreover, the obtained results showed that, referring to the considered database, the intercept cohesion was the most significant parameter in defining the class of each slope, whereas the unit weight had the smallest influence. Finally, the obtained results showed that the Harmony Search algorithm is an efficient approach for training K-means algorithms.

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“…Artificial intelligence techniques are one of the most popular ways to solve complex problems in industry and economics sectors (Mahdevari et al 2017 Naderpour et al 2019;Zhang and Geem 2019;Kayabekir et al 2020;Daneshvar and Behnood 2020;Guido et al 2020a;Kandiri et al 2020). In recent years, several studies have been conducted on the application of artificial intelligence in engineering problems (Geem and Kim 2018;Mikaeil et al 2018cMikaeil et al , 2019bSalemi et al 2018;Gnawali et al 2019;Park et al 2020;Shaffiee Haghshenas et al 2020;Noori et al 2020;Fiorini Morosini et al 2020;Guido et al 2020b). One of the most efficient methods of artificial intelligence is the imperialist competitive algorithm (ICA), suggested by Atashpaz-Gargari and Lucas (Khabbazi et al 2009;Haghshenas et al 2017).…”

Section: Imperialist Competitive Algorithmmentioning

confidence: 99%

Assessing the System Vibration of Circular Sawing Machine in Carbonate Rock Sawing Process Using Experimental Study and Machine Learning

et al. 2021

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Predicting the vibration of the circular sawing machine is very important in examining the performance of the sawing process, as it shows the amount of energy consumption of the circular sawing machine. Also, this factor is directly related to maintenance cost, such that with a small increase in the level of vibration, the maintenance cost increases to a large extent. This paper presents new prediction models to assess the vibration of circular sawing machine. An evaluation model based on the imperialist competitive algorithm as one of the most efficient artificial intelligence techniques was used for estimation of sawability of the dimension stone in carbonate rocks. For this purpose, four main physical and mechanical properties of rock including Schimazek’s F-abrasivity, uniaxial compressive strength, mean Mohs hardness, and Young’s modulus as well as two operational parameters of circular sawing machine including depth of cut and feed rate, were investigated and measured. In the predicted model, the system vibration in stone sawing was considered as a dependent variable. The results showed that the system vibration can be investigated using the newly developed machine learning models. It is very suitable to assess the system vibration based on the mechanical properties of rock and operational properties.

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Economic Dispatch Optimization of Multi-Water Resources: A Case Study of an Island in South Korea

Cited by 12 publications

References 30 publications

Development of a Binary Model for Evaluating Water Distribution Systems by a Pressure Driven Analysis (PDA) Approach

Development of a Binary Model for Evaluating Water Distribution Systems by a Pressure Driven Analysis (PDA) Approach

Application of Harmony Search Algorithm to Slope Stability Analysis

Assessing the System Vibration of Circular Sawing Machine in Carbonate Rock Sawing Process Using Experimental Study and Machine Learning

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