Applications of fuzzy logic in renewable energy systems – A review
TL;DR: An attempt has been made to review the applications of fuzzy logic based models in renewable energy systems namely solar, wind, bio-energy, micro-grid and hybrid applications and indicates that fuzzy based models provide realistic estimates.
Abstract: In recent years, with the advent of globalization, the world is witnessing a steep rise in its energy consumption. The world is transforming itself into an industrial and knowledge society from an agricultural one which in turn makes the growth, energy intensive resulting in emissions. Energy modeling and energy planning is vital for the future economic prosperity and environmental security. Soft computing techniques such as fuzzy logic, neural networks, genetic algorithms are being adopted in energy modeling to precisely map the energy systems. In this paper, an attempt has been made to review the applications of fuzzy logic based models in renewable energy systems namely solar, wind, bio-energy, micro-grid and hybrid applications. It is found that fuzzy based models are extensively used in recent years for site assessment, for installing of photovoltaic/wind farms, power point tracking in solar photovoltaic/wind, optimization among conflicting criteria. The review indicates that fuzzy based models provide realistic estimates.
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TL;DR: This paper reviews the urban potential of AI and proposes a new framework binding AI technology and cities while ensuring the integration of key dimensions of Culture, Metabolism and Governance which are known to be primordial in the successful integration of Smart Cities for the compliance to the Sustainable Development Goal 11 and the New Urban Agenda.
497 citations
TL;DR: The heuristic and hybrid approaches utilized in ANFIS training are examined in order to guide researchers in their study and it has been observed that there is a trend toward heuristic based ANfIS training algorithms for better performance recently.
Abstract: In the structure of ANFIS, there are two different parameter groups: premise and consequence. Training ANFIS means determination of these parameters using an optimization algorithm. In the first ANFIS model developed by Jang, a hybrid learning approach was proposed for training. In this approach, while premise parameters are determined by using gradient descent (GD), consequence parameters are found out with least squares estimation (LSE) method. Since ANFIS has been developed, it is used in modelling and identification of numerous systems and successful results have been achieved. The selection of optimization method utilized in training is very important to get effective results with ANFIS. It is seen that derivate based (GD, LSE etc.) and non-derivative based (heuristic algorithms such us GA, PSO, ABC etc.) algorithms are used in ANFIS training. Nevertheless, it has been observed that there is a trend toward heuristic based ANFIS training algorithms for better performance recently. At the same time, it seems to be proposed in derivative and heuristic based hybrid algorithms. Within the scope of this study, the heuristic and hybrid approaches utilized in ANFIS training are examined in order to guide researchers in their study. In addition, the final status in ANFIS training is evaluated and it is aimed to shed light on further studies related to ANFIS training.
454 citations
Cites methods from "Applications of fuzzy logic in rene..."
...Therefore, it has been used in solving many problems (Adnan et al. 2015; Azadegan et al. 2011; Koukol et al. 2015; Lochan and Roy 2015; Suganthi et al. 2015)....
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TL;DR: A hybrid algorithm that combines similar days (SD) selection, empirical mode decomposition (EMD), and long short-term memory (LSTM) neural networks to construct a prediction model (i.e., SD-EMD-L STM) for short- term load forecasting is presented.
Abstract: Accurate load forecasting is an important issue for the reliable and efficient operation of a power system. This study presents a hybrid algorithm that combines similar days (SD) selection, empirical mode decomposition (EMD), and long short-term memory (LSTM) neural networks to construct a prediction model (i.e., SD-EMD-LSTM) for short-term load forecasting. The extreme gradient boosting-based weighted k-means algorithm is used to evaluate the similarity between the forecasting and historical days. The EMD method is employed to decompose the SD load to several intrinsic mode functions (IMFs) and residual. Separated LSTM neural networks were also employed to forecast each IMF and residual. Lastly, the forecasting values from each LSTM model were reconstructed. Numerical testing demonstrates that the SD-EMD-LSTM method can accurately forecast the electric load.
427 citations
Cites methods from "Applications of fuzzy logic in rene..."
...The AI-based methods, such as the artificial neural networks [11–14], support vector machine [15–18], expert system models [19], fuzzy logistic methods [20], and Bayesian neural networks [21], are extensively used to handle many forecasting problems....
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TL;DR: A review of the current state of the art in decision support methods applied to renewable and sustainable energy throughout the literature in the field of energy planning is presented in this article, where selected papers were classified by their year of publication, decision making technique, energy type, the criteria used, geographic distribution and the application areas.
Abstract: One of the problems facing researchers in the application of renewable energy systems is that the evaluation of the sustainability is extremely perplex. Decision making in energy projects requires consideration of technical, economic, environmental and social impacts and is often complicated. This paper presents a review of the current state of the art in decision support methods applied to renewable and sustainable energy throughout the literature in the field of energy planning. The selected papers were classified by their year of publication, decision making technique, energy type, the criteria used, geographic distribution and the application areas.
300 citations
TL;DR: This paper provides a comprehensive review on previous and current research related to the HEMS by considering various DR programs, smart technologies, and load scheduling controllers.
Abstract: The increasing demand for electricity and the emergence of smart grids have presented new opportunities for a home energy management system (HEMS) that can reduce energy usage. The HEMS incorporates a demand response (DR) tool that shifts and curtails demand to improve home energy consumption. This system commonly creates optimal consumption schedules by considering several factors, such as energy costs, environmental concerns, load profiles, and consumer comfort. With the deployment of smart meters, performing load control using the HEMS with DR-enabled appliances has become possible. This paper provides a comprehensive review on previous and current research related to the HEMS by considering various DR programs, smart technologies, and load scheduling controllers. The application of artificial intelligence for load scheduling controllers, such as artificial neural network, fuzzy logic, and adaptive neural fuzzy inference system, is also reviewed. Heuristic optimization techniques, which are widely used for optimal scheduling of various electrical devices in a smart home, are also discussed.
281 citations
Cites background from "Applications of fuzzy logic in rene..."
...FLC is simple to execute, and it can handle nonlinear and linear systems based on linguistic rules; furthermore, it requires no mathematical model [101]–[103]....
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References
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TL;DR: In this paper, the authors proposed an integrated VIKOR-AHP methodology to determine the best renewable energy alternative for Turkey by using pairwise comparison matrices of AHP.
616 citations
TL;DR: The main techniques that will be deliberated are the Perturb and Observe, Incremental Conductance and Hill Climbing, as well as the more recent MPPT approaches using soft computing methods such as Fuzzy Logic Control, Artificial Neural Network and Evolutionary Algorithms.
Abstract: This paper presents a review on the state-of-the-art maximum power point tracking (MPPT) techniques for PV power system applications. The main techniques that will be deliberated are the Perturb and Observe, Incremental Conductance and Hill Climbing. The coverage will also encompass their variations and adaptive forms. In addition, the more recent MPPT approaches using soft computing methods such as Fuzzy Logic Control, Artificial Neural Network and Evolutionary Algorithms are included. Whilst the paper provides as thorough treatment of MPPT at normal (uniform) insolation, its focus will be on the applications of the abovementioned techniques during partial shading conditions. It is envisaged that this review work will be a source of valuable information for PV professionals to keep abreast with the latest progress in this area, as well as for new researchers to get started on MPPT.
508 citations
TL;DR: In this paper, an expert multi-objective AMPSO (Adaptive Modified Particle Swarm Optimization algorithm) is presented for optimal operation of a typical MG with RESs (renewable energy sources) accompanied by a back-up Micro-Turbine/Fuel Cell/Battery hybrid power source to level the power mismatch or to store the surplus of energy when it's needed.
481 citations
TL;DR: In this paper, fuzzy multicriteria decision-making methodologies are suggested for the selection among renewable energy alternatives, which are based on axiomatic design (AD) and analytic hierarchy process (AHP).
404 citations
TL;DR: In this paper, the authors assess different MPPT techniques, provide background knowledge, implementation topology, grid interconnection of PV and solar microinverter requirements presented in the literature, doing depth comparisons between them with a brief discussion.
Abstract: The photovoltaic (PV) system is one of the renewable energies that attract the attention of researchers in the recent decades. The PV generators exhibit nonlinear I–V and P–V characteristics. The maximum power produced varies with both irradiance and temperature. Since the conversion efficiency of PV arrays is very low, it requires maximum power point tracking (MPPT) control techniques. The maximum power point tracking (MPPT) is the automatic control algorithm to adjust the power interfaces and achieve the greatest possible power harvest, during moment to moment variations of light level, shading, temperature, and photovoltaic module characteristics. The purpose of the MPPT is to adjust the solar operating voltage close to the MPP under changing atmospheric conditions. It has become an essential component to evaluate the design performance of PV power systems. This investigation aims to assess different MPPT techniques, provide background knowledge, implementation topology, grid interconnection of PV and solar microinverter requirements presented in the literature, doing depth comparisons between them with a brief discussion. The MPPT merits, demerits and classification, which can be used as a reference for future research related to optimizing the solar power generation, are also discussed. Conventional methods are easy to implement but they suffer from oscillations at MPP and tracking speed is less due to fixed perturb step. Intelligent methods are efficient; oscillations are lesser at MPP in steady state and tracked quickly in comparison to conventional methods.
400 citations