Search

This article presents an algorithm for modelling an Adaptive Neuro Fuzzy Inference System (ANFIS) for power transformer paper conditions in order to estimate the transformer's expected life. The dielectric characteristics, dissolved gasses, and furfural of 108 running transformers were collected, which were divided into 76 training datasets and another 32 testing datasets. The degree of polymerization (DP) of the transformer paper was predicted using the ANFIS model based on using the dielectric characteristics and dissolved gases as input. These inputs were analyzed, and the best combination was selected, whereas CO + CO2, acidity, interfacial tension, and color were correlated with the paper's deterioration condition and were chosen as the input variables. The best combination of input variables and membership function was selected to build the optimal ANFIS model, which was then compared and evaluated. The proposed ANFIS model has 89.07% training accuracy and 85.75% testing accuracy and was applied to a transformer paper insulation assessment and an estimation of the expected life of four Indonesian transformers for which furfural data is unavailable. This proposed algorithm can be used as a furfural alternative for the general assessment of transformer paper conditions and the estimation of expected life and provides a helpful assistance for experts in transformer condition assessment. [ABSTRACT FROM AUTHOR]

The digitization of distribution power systems has revolutionized the way data are collected and analyzed. In this paper, the critical task of harnessing this information to identify irregularities and anomalies in electricity consumption is tackled. The focus is on detecting non-technical losses (NTLs) and energy theft within distribution networks. A comprehensive overview of the methodologies employed to uncover NTLs and energy theft is presented, leveraging measurements of electricity consumption. The most common scenarios and prevalent cases of anomalies and theft among consumers are identified. Additionally, statistical indicators tailored to specific anomalies are proposed. In this research paper, the practical implementation of numerous artificial intelligence (AI) algorithms, including the artificial neural network (ANN), ANFIS, autoencoder neural network, and K-mean clustering, is highlighted. These algorithms play a central role in our research, and our primary objective is to showcase their effectiveness in identifying NTLs. Real-world data sourced directly from distribution networks are utilized. Additionally, we carefully assess how well statistical methods work and compare them to AI techniques by testing them with real data. The artificial neural network (ANN) accurately identifies various consumer types, exhibiting a frequency error of 7.62%. In contrast, the K-means algorithm shows a slightly higher frequency error of 9.26%, while the adaptive neuro-fuzzy inference system (ANFIS) fails to detect the initial anomaly type, resulting in a frequency error of 11.11%. Our research suggests that AI can make finding irregularities in electricity consumption even more effective. This approach, especially when using data from smart meters, can help us discover problems and safeguard distribution networks. [ABSTRACT FROM AUTHOR]

This paper presents a model for determining the availability of continuous systems at open pits using the neuro-fuzzy system. The concept of availability is divided into partial indicators (synthetic indicators and sub-indicators). The presented model in relation to already existing models for determining availability uses a combination of the advantages of artificial neural networks and fuzzy logic. The case study addressed the I ECC (bucket wheel excavator–conveyors–crushing plant) system of the open pit Drmno, Kostolac. In this paper, in addition to the ANFIS model for determining the availability of continuous systems, a simulation model was developed. The obtained results of the ANFIS model were verified with the help of a simulation model that uses certain assumptions about the distribution of failures. This paper was created as a result of several years of field and theoretical research into the availability of continuous systems in open pits, and completes a cycle that consists of several published articles on the subject of modeling the behavior of these systems in real time using a time picture of the state, expert assessment, simulation and AI models, while respecting the multidisciplinarity of the problem (mining technological, mechanical, and information technological aspects). The developed ANFIS model is a key instrument for improving operational efficiency and resource management in the mining sector. Its ability to accurately predict the availability of the ECC system brings not only operational benefits through reduced downtime and optimized maintenance, but also a potential reduction in overall costs at coal open pits. Such an innovative model marks a significant step forward in the mining industry, especially when it comes to continuous systems in coal open pits. [ABSTRACT FROM AUTHOR]

This paper presents a model for determining fuzzy evaluations of partial indicators of the availability of continuous systems at coal open pits using a neuro-fuzzy inference system. The system itself is a combination of fuzzy logic and artificial neural networks. The system availability is divided into partial indicators. By combining the fuzzy logic and artificial neural networks, a model is obtained that has the ability to learn and uses expert judgment for that learning. This paper deals with the ECC system (bucket wheel excavator-conveyor-crushing plant) of the open pit Drmno-Kostolac, which operates within the Electric Power Company of Serbia. The advantage of a model of this type is that it does not rely on the historical experiences of experts and usual predicted values for the fuzzy evaluation of partial indicators, which are based on the assumption that similar systems affect availability in a similar way. The fuzzy evaluation of partial indicators is based on historical data for the specific system for which the model was created. As such, it can more accurately predict continuous systems availability on the basis of expert evaluations in the appropriate time period. Another advantage of this model is that the availability is estimated on a quarterly basis, which gives a more accurate view because it uses a smaller time period with more similar characteristics and, thus, includes certain external influences which are related to the quarterly meteorological conditions. [ABSTRACT FROM AUTHOR]

The main aim of the research work presented in this paper consists of proposing an effective control scheme for a grid-connected single-phase photovoltaic (PV) system to enhance not only the power quality at the point of common coupling (PCC) but also to operate with a maximum power point tracking (MPPT) controller. Moreover, an orthogonal signal generator (OSG) module for effective grid synchronization, a current reference generation controller, and a PWM generating block have also been designed and included in this paper. The proposed control strategy allows the MPPT controller to switch to faulty mode and maintains the voltage according to network requirements using an adaptive neuro-fuzzy inference system (ANFIS)-based control whenever a fault occurs at the PCC. The performance of the analyzed control strategy, which is based on the static compensation of the DC-link voltage fluctuations in a grid-connected inverter powered by PV, is further explored through simulations in MATLAB, and the results are included in this paper. Moreover, the control scheme is implemented experimentally using a dSPACE DS 1104 control board and then assessed on a small laboratory-scale single-phase PV system that is subjected to some fault scenarios. The simulation and experimental results have shown improved power quality and robustness against grid fluctuations, resulting in better dynamic performance. [ABSTRACT FROM AUTHOR]

The characteristics of a PV (photovoltaic) module is non-linear and vary with nature. The tracking of maximum power point (MPP) at various atmospheric conditions is essential for the reliable operation of solar-integrated power generation units. This paper compares the most widely used maximum power point tracking (MPPT) techniques such as the perturb and observe method (P&O), incremental conductance method (INC), fuzzy logic controller method (FLC), neural network (NN) model, and adaptive neuro-fuzzy inference system method (ANFIS) with the modern approach of the hybrid method (neural network + P&O) for PV systems. The hybrid method combines the strength of the neural network and P&O in a single framework. The PV system is composed of a PV panel, converter, MPPT unit, and load modelled using MATLAB/Simulink. These methods differ in their characteristics such as convergence speed, ease of implementation, sensors used, cost, and range of efficiencies. Based on all these, performances are evaluated. In this analysis, the drawbacks of the methods are studied, and wastage of the panel's available output energy is observed. The hybrid technique concedes a spontaneous recovery during dynamic changes in environmental conditions. The simulation results illustrate the improvements obtained by the hybrid method in comparison to other techniques. [ABSTRACT FROM AUTHOR]

The characterization of wind speed distribution and the optimal assessment of wind energy potential are critical factors in selecting a suitable site for wind power plants (WPP). The Weibull distribution law has been used extensively to analyze the wind characteristics of candidate WPP sites, and to estimate the available and deliverable energy. This paper presents a comparative study of five wind energy resource assessment methods as they applied to the context of wind sites in West Sub-Saharan Africa. We investigated three numerical approaches, namely, the adaptive neuro-fuzzy inference system (ANFIS), the multilayer perceptron method (MLP), and support vector regression (SVR), to derive the distribution law of wind speeds and to optimally quantify the corresponding wind energy potential. Next, we compared these three approaches to two well-known Weibull distribution law-based methods: the empirical method of Justus (EMJ) and the maximum likelihood method (MLM). Case study results indicated that the neural network-based methods, ANFIS and MLP, yielded the most accurate distribution fits and wind energy potential estimates, and consequently, are the most recommended methods for the wind sites in Togo and Benin. The orders of magnitude of the root mean squared error (RMSE) in estimating the recoverable energy using ANFIS were, respectively, 10-4 and 10-5 for Lomé and Cotonou, while MLP achieved an RMSE order of magnitude of 10-3 for both sites. [ABSTRACT FROM AUTHOR]

The foundation of energy-efficient architectural design is modeling heating and cooling loads (HLs and CLs), which defines the heating and cooling apparatus constraints necessary to maintain a suitable interior air environment. It is possible that analytical models for energy-efficient buildings might offer an accurate evaluation of the influence that various building designs would have. The implementation of these instruments, however, might be a process that requires a significant amount of manual labor, a significant amount of time, and is reliant on user experiences. In light of this, the authors of this paper present two unique methods for estimating the CL of residential structures in the form of complex mathematical concepts. These methodologies include an evolutionary web algorithm (EWA), biogeography-based optimization (BBO), and a hybridization of an adaptive neuro-fuzzy interface system (ANFIS), namely BBO-ANFIS and EWA-ANFIS. The findings initiated from each of the suggested models are evaluated with the help of various performance metrics. Moreover, it is possible to determine which model is the most effective by comparing their coefficient of determination ( R 2   ) and its root mean square error (RMSE) to each other. In mapping non-linear connections between input and output variables, the observed findings showed that the models used have a great capability. In addition, the results showed that BBO-ANFIS was the superior forecasting model out of the two provided models, with the lowest value of RMSE and the greatest value of R 2   (RMSE = 0.10731 and 0.11282 and R 2   = 0.97776 and 0.97552 for training and testing phases, respectively). The EWA-ANFIS also demonstrated RMSE and R 2   values of 0.18682 and 0.17681 and 0.93096 and 0.93874 for the training and testing phases, respectively. Finally, this study has proven that ANN is a powerful tool and will be useful for predicting the CL in residential buildings. [ABSTRACT FROM AUTHOR]

Wind is a renewable and green energy source that is vital for sustainable human development. Wind variability implies that wind power is random, intermittent, and volatile. For the reliable, stable, and secure operation of an electrical grid incorporating wind power systems, a multi-hour ahead wind power forecasting system comprising a physics-based model, a multi-criteria decision making scheme, and two artificial intelligence models was proposed. Specifically, a Weather Research and Forecasting (WRF) model was used to produce wind speed forecasts. A technique for order of preference by similarity to ideal solution (TOPSIS) scheme was employed to construct a 5-in-1 (ensemble) WRF model relying on 1334 initial ensemble members. Two adaptive neuro-fuzzy inference system (ANFIS) models were utilised to correct the wind speed forecasts and determine a power curve model converting the improved wind speed forecasts to wind power forecasts. Moreover, three common statistics-based forecasting models were chosen as references for comparing their predictive performance with that of the proposed WRF-TOPSIS-ANFIS model. Using a set of historical wind data obtained from a wind farm in China, the WRF-TOPSIS-ANFIS model was shown to provide good wind speed and power forecasts for 30-min to 24-h time horizons. This paper demonstrates that the novel forecasting system has excellent predictive performance and is of practical relevance. [ABSTRACT FROM AUTHOR]

The power management strategy (PMS) is intimately linked to the fuel economy in the hybrid electric vehicle (HEV). In this paper, a hybrid power management scheme is proposed; it consists of an adaptive neuro-fuzzy inference method (ANFIS) and the equivalent consumption minimization technique (ECMS). Artificial intelligence (AI) is a key development for managing power among various energy sources. The hybrid power supply is an eco-acceptable system that includes a proton exchange membrane fuel cell (PEMFC) as a primary source and a battery bank and ultracapacitor as electric storage systems. The Haar wavelet transform method is used to calculate the stress σ on each energy source. The proposed model is developed in MATLAB/Simulink software. The simulation results show that the proposed scheme meets the power demand of a typical driving cycle, i.e., Highway Fuel Economy Test Cycle (HWFET) and Worldwide Harmonized Light Vehicles Test Procedures (WLTP—Class 3), for testing the vehicle performance, and assessment has been carried out for various PMS based on the consumption of hydrogen, overall efficiency, state of charge of ultracapacitors and batteries, stress on hybrid sources and stability of the DC bus. By combining ANFIS and ECMS, the consumption of hydrogen is minimized by 8.7% compared to the proportional integral (PI), state machine control (SMC), frequency decoupling fuzzy logic control (FDFLC), equivalent consumption minimization strategy (ECMS) and external energy minimization strategy (EEMS). [ABSTRACT FROM AUTHOR]

Machine learning (ML) models have been widely used in the modeling, design and prediction in energy systems. During the past two decades, there has been a dramatic increase in the advancement and application of various types of ML models for energy systems. This paper presents the state of the art of ML models used in energy systems along with a novel taxonomy of models and applications. Through a novel methodology, ML models are identified and further classified according to the ML modeling technique, energy type, and application area. Furthermore, a comprehensive review of the literature leads to an assessment and performance evaluation of the ML models and their applications, and a discussion of the major challenges and opportunities for prospective research. This paper further concludes that there is an outstanding rise in the accuracy, robustness, precision and generalization ability of the ML models in energy systems using hybrid ML models. Hybridization is reported to be effective in the advancement of prediction models, particularly for renewable energy systems, e.g., solar energy, wind energy, and biofuels. Moreover, the energy demand prediction using hybrid models of ML have highly contributed to the energy efficiency and therefore energy governance and sustainability. [ABSTRACT FROM AUTHOR]

Classic and intelligent techniques aim to locate and track the maximum power point of photovoltaic (PV) systems, such as perturb and observe (P&O), fuzzy logic (FL), artificial neural networks (ANNs), and adaptive neuro-fuzzy inference systems (ANFISs). This paper proposes and compares three intelligent algorithms for maximum power point tracking (MPPT) control, specifically fuzzy, ANN, and ANFIS. The modeling of a single-diode equivalent circuit-based 3 kWp PV plant was developed and validated to achieve this purpose. Then, the MPPT techniques were designed and applied to control the buck–boost converter's switching device of the PV plant. All three methods use the ambient conditions as input variables: solar irradiance and ambient temperature. The proposed methodology comprises the study of the dynamic response for tracking the maximum power point and the power generated of the PV systems, and it was compared to the classic P&O technique under varying ambient conditions. We observed that the intelligent techniques outperformed the classic P&O method in tracking speed, tracking accuracy, and reducing oscillation around the maximum power point (MPP). The ANN technique was the better control algorithm in energy gain, managing to recover up to 9.9% power. [ABSTRACT FROM AUTHOR]

In this research paper, a hybrid Artificial Neural Network (ANN)-Fuzzy Logic Control (FLC) tuned Flower Pollination Algorithm (FPA) as a Maximum Power Point Tracker (MPPT) is employed to amend root mean square error (RMSE) of photovoltaic (PV) modeling. Moreover, Gaussian membership functions have been considered for fuzzy controller design. This paper interprets the Luo converter occupied brushless DC motor (BLDC)-directed PV water pump application. Experimental responses certify the effectiveness of the suggested motor-pump system supporting diverse operating states. The Luo converter, a newly developed DC-DC converter, has high power density, better voltage gain transfer and superior output waveform and can track optimal power from PV modules. For BLDC speed control there is no extra circuitry, and phase current sensors are enforced for this scheme. The most recent attempt using adaptive neuro-fuzzy inference system (ANFIS)-FPA-operated BLDC directed PV pump with advanced Luo converter, has not been formerly conferred. [ABSTRACT FROM AUTHOR]

Nowadays, the growth in the consumption of energy and the need to face pollution resulting from its generation are causing concern for consumers and providers. Energy consumption in residential buildings and houses is about 22% of total energy production. Cutting-edge energy managers aim to optimize electrical devices in homes, taking into account users' patterns, goals, and needs, by creating energy consumption awareness and helping current change habits. In this way, energy manager systems (EMSs) monitor and manage electrical appliances, automate and schedule actions, and make suggestions regarding electrical consumption. Furthermore, gamification strategies may change energy consumption patterns through energy managers, which are seen as an option to save energy and money. Therefore, this paper proposes a personalized gamification strategy for an EMS through an adaptive neuro-fuzzy inference system (ANFIS) decision-making engine to classify the level of electrical consumption and persuade the end-user to reduce and modify consumption patterns, saving energy and money with gamified motivations. These strategies have proven to be effective in changing consumer behavior with intrinsic and extrinsic motivations. The interfaces consider three cases for summer and winter periods to calculate the saving-energy potentials: (1) for a type of user that is interested in home-improvement efforts while helping to save energy; (2) for a type of user that is advocating to save energy; (3) for a type of user that is not interested in saving energy. Hence, each interface considers the end-user's current consumption and the possibility to modify their consumption habits using their current electrical devices. Finally, an interface displaying the electrical consumption for each case exemplifies its linkage with EMSs. [ABSTRACT FROM AUTHOR]

High quality of solar radiation data is essential for solar resource assessment. For remote areas this is a challenge, as often only satellite data with low spatial resolution are available. This paper presents an interpolation method based on topographic data in digital elevation model format to improve the resolution of solar radiation maps. The refinement is performed with a data mining method based on first-order Sugeno type Adaptive Neuro-Fuzzy Inference System. The training set contains topographic characteristics such as terrain aspect, slope and elevation which may influence the solar radiation distribution. An efficient sampling method is proposed to obtain representative training sets from digital elevation model data. The proposed geographic information system based approach makes this method reproducible and adaptable for any region. A case study is presented on the remote Amhara region in North Shewa, Ethiopia. Results are shown for interpolation of solar radiation data from 10 km × 10 km to a resolution of 1 km × 1 km and are validated with data from the PVGIS and SWERA projects. [ABSTRACT FROM AUTHOR]

Innovations are required for electric vehicles (EVs) to be lighter and more energy efficient due to the range anxiety issue. This article introduces an intelligent control of an organic structure solar supercapacitor (OSSC) for EVs to meet electrical load demands with solar renewable energy. A carbon fibre-reinforced polymer, nano zinc oxide (ZnO), and copper oxide (CuO) fillers have been used in the development of OSSC prototypes. The organic solar cell, electrical circuits, converter, controller, circuit breaker switch, and batteries were all integrated for the modelling of OSSCs. A carbon fibre (CF)-reinforced CuO-doped polymer was utilised to improve the concentration of electrons. The negative electrodes of the CF were strengthened with nano ZnO epoxy to increase the mobility of electrons as an n-type semiconductor (energy band gap 3.2–3.4 eV) and subsequently increased to 3.5 eV by adding 6% π-carbon. The electrodes of the CF were strengthened with epoxy-filled nano-CuO as a p-type semiconductor to facilitate bore/positive charging. They improve the conductivity of the OSSC. The OSSC power storage was controlled by an adaptive neuro-fuzzy intelligent system controller to meet the load demand of EVs and auxiliary battery charging. Moreover, a fully charged OSSC (solar irradiance = 1000 W/m2) produced 561 W·h/m2 to meet the vehicle load demand with 45 A of auxiliary battery charging current. Therefore, the OSSC can save 15% in energy efficiency and contribute to emission control. The integration of an OSSC with an EV battery can minimise the weight and capacity of the battery by 7.5% and 10%, respectively. [ABSTRACT FROM AUTHOR]

The development of electric vehicles (EVs) has drawn considerable attention to the establishment of sustainable transport systems to enable improvements in energy optimization and air quality. EVs are now widely used by the public as one of the sustainable transportation measures. Nevertheless, battery charging for EVs create several challenges, for example, lack of charging facilities in urban areas and expensive battery maintenance. Among various components in EVs, the battery pack is one of the core consumables, which requires regular inspection and repair in terms of battery life cycle and stability. The charging efficiency is limited to the power provided by the facilities, and therefore the current business model for EVs is not sustainable. To further improve its sustainability, plug-in electric vehicle battery pack standardization (PEVBPS) is suggested to provide a uniform, standardized and mobile EV battery that is managed by centralized service providers for repair and maintenance tasks. In this paper, a fuzzy-based battery life-cycle prediction framework (FBLPF) is proposed to effectively manage the PEVBPS in the market, which integrates the multi-responses Taguchi method (MRTM) and the adaptive neuro-fuzzy inference system (ANFIS) as a whole for the decision-making process. MRTM is formulated based on selection of the most relevant and critical input variables from domain experts and professionals, while ANFIS takes part in time-series forecasting of the customized product life-cycle for demand and electricity consumption. With the aid of the FPLCPF, the revolution of the EV industry can be revolutionarily boosted towards total sustainable development, resulting in pro-active energy policies in the PEVBPS eco-system. [ABSTRACT FROM AUTHOR]

The short-term variability of photovoltaic (PV) system-generated power due to ambient conditions, such as passing clouds, represents a key challenge for network planners and operators. Such variability can be reduced using a geographical smoothing technique based on installing multiple PV systems over certain locations at distances of meters to kilometers. To accurately estimate the PV system's generated power during cloud events, a variability reduction index (VRI), which is a function of several parameters, should be calculated precisely. In this paper, the Wavelet Transform Technique (WTT) along with Adaptive Neuro Fuzzy Inference System (ANFIS) are used to develop new models to estimate the PV system's power output during cloud events. In this context, irradiance data collected from one PV system along with other parameters, including ambient conditions, were used to develop the proposed models. Ultimately, the models were validated through their application on a 0.7 km2 PV plant with 16 rooftop PV systems in Brisbane, Australia. [ABSTRACT FROM AUTHOR]

Heating and cooling systems account for a considerable portion of the energy consumed for domestic reasons in Europe. Burning fossil fuels is the main way to produce this energy, which has a detrimental effect on the environment. It is essential to consider a building's characteristics when determining how much heating and cooling is necessary. As a result, a study of the related buildings' characteristics, such as the type of cooling and heating systems required for maintaining appropriate indoor air conditions, can help in the design and construction of energy-efficient buildings. Numerous studies have used machine learning to predict cooling and heating systems based on variables that include relative compactness, orientation, overall height, roof area, wall area, surface area, glazing area, and glazing area distribution. Fuzzy logic, however, is not used in any of these methods. In this article, we study a fuzzy logic approach, i.e., HHO−ANFIS (combination of Harris hawks optimization and adaptive neuro-fuzzy interface system), to predict the heating load in residential buildings and investigate the feasibility of this technique in predicting the heating load. Fuzzy techniques obtain perfect results. The analysis results show that the HHO−ANFIS with a population size of 400, the highest value of R2 (0.98709 and 0.98794), and the lowest value of RMSE (0.08769 and 0.08281) in the training and testing dataset, respectively, can predict the heating load with high accuracy. According to the high value of R2 (98%) and low value of RMSE, HHO−ANFIS can be used in predicting the heating load of residential buildings. [ABSTRACT FROM AUTHOR]

Featured Application: This work describes the design of an intelligent tool for hydrophobicity classification of polymeric insulators used in electrical transmission lines. It eliminates the manual inspection of insulators, which involves a significant amount of labor work load. This automated tool can be integrated to an unmanned aerial vehicle to provide autonomous inspection of insulators. Hydrophobicity of polymeric insulator plays a vital role in determining the insulation quality in outdoor overhead electrical transmission and distribution lines. Loss of hydrophobicity increases the leakage current and leads to flashover. Monitoring hydrophobicity becomes a fundamental requirement to ensure continuity of power line operations. Hydrophobicity of polymeric insulator is classified according to STRI (Swedish Transmission Research Institute) guidelines. This paper proposes an intelligent ANFIS (Adaptive Neuro-Fuzzy Inference System) based classifier to determine the hydrophobicity quality using the digital image of the insulator. Ten statistical features are extracted from the digital images. Two stages of feature reduction are employed to reduce the number of features. Pre-design stage uses PCA (Principal Component Analysis) and reduces the number of features to six from ten and the post-design stage analyzes the accumulation effect to reduce the number of features to four. Various ANFIS classifiers are trained using these reduced features extracted from the image. The performance of these ANFIS classifiers is evaluated in both field and laboratory specimens. Results indicate classification accuracy of 96.4% and 93.3% during the training and testing phase when triangular membership function with linear output function is employed in ANFIS. A GUI (Graphical User Interface) has also been designed to facilitate the use of the proposed system by field operators. [ABSTRACT FROM AUTHOR]

Analyzing electrical power generation for a wind turbine has associated inaccuracies due to fluctuations in environmental factors, mechanical alterations of wind turbines, and natural disaster. Thus, development of a highly reliable prediction model based on climatic conditions is crucial in forecasting electrical power for proper management of energy demand and supply. This is essential because early forecasting systems will enable an energy supplier to schedule and manage resources efficiently. In this research, we have put forward a novel electrical power prediction model using wavelet and particle swarm optimization based dual-stage adaptive neuro-fuzzy inference system (dual-stage Wavelet-PSO-ANFIS) for precise estimation of electrical power generation based on climatic factors. The first stage is used to project wind speed based on meteorological data available, while the second stage took the output wind speed prediction to predict electrical power based on actual supervisory control and data acquisition (SCADA). Furthermore, influence of data dependence on the forecasting accuracy for both stages is analyzed using a subset of data as input to predict the wind power which was also compared with other existing electrical power forecasting techniques. This paper defines the basic framework and the performance evaluation of a dual-stage Wavelet-PSO-ANFIS based electrical power forecasting system using a practical implementation. [ABSTRACT FROM AUTHOR]

In the industrial world, parallel pump systems are frequently employed. Due to various reasons, the pumps are frequently operated outside their intended parameters, which reduces their efficiency and performance. To operate the pump system with optimum efficiency, the pumps and their speed selection are mandatory. This research presents an optimum switching technique for variable speed pumping stations with multi-parallel pump combinations to enhance energy savings. The proposed optimal control system is designed in such a way as to decrease overall losses in the pump system. The effectiveness of the proposed method is investigated on a real scale of a multi-parallel pump drive system in a Matlab Simulink environment, and experimental validation is performed in a laboratory prototype. The suggested approach enhances power savings and shall be adapted for various pumping applications. [ABSTRACT FROM AUTHOR]

This paper proposes an adaptive neuro-fuzzy inference system (ANFIS) maximum power point tracking (MPPT) controller for grid-connected doubly fed induction generator (DFIG)-based wind energy conversion systems (WECS). It aims at extracting maximum power from the wind by tracking the maximum power peak regardless of wind speed. The proposed MPPT controller implements an ANFIS approach with a backpropagation algorithm. The rotor speed acts as an input to the controller and torque reference as the controller's output, which further inputs the rotor side converter's speed control loop to control the rotor's actual speed by adjusting the duty ratio for the rotor side converter. The grid partition method generates input membership functions by uniformly partitioning the input variable ranges and creating a single-output Sugeno fuzzy system. The neural network trained the fuzzy input membership according to the inputs and alter the initial membership functions. The simulation results have been validated on a 2 MW wind turbine using the MATLAB/Simulink environment. The controller's performance is tested under various wind speed circumstances and compared with the performance of a conventional proportional–integral MPPT controller. The simulation study shows that WECS can operate at its optimum power for the proposed controller's wide range of input wind speed. [ABSTRACT FROM AUTHOR]

This paper contains studies of daily energy production forecasting methods for photovoltaic solar panels (PV panel) by using mathematical methods and fuzzy logic models. Mathematical models are based on analytic equations that bind PV panel power with temperature and solar radiation. In models based on fuzzy logic, we use Adaptive-network-based Fuzzy Inference Systems (ANFIS) and the zero-order Takagi-Sugeno model (TS) with specially selected linear and non-linear membership functions. The use of mentioned membership functions causes that the TS system is equivalent to a polynomial and its properties can be compared to other analytical models of PV panels found in the literature. The developed models are based on data from a real system. The accuracy of developed prognostic models is compared, and a prototype software implementing the best-performing models is presented. The software is written for a generic programmable logic controller (PLC) compliant to the IEC 61131-3 standard. [ABSTRACT FROM AUTHOR]

The study targeted towards drying of cantaloupe slices with various thicknesses in a microwave dryer. The experiments were carried out at three microwave powers of 180, 360, and 540 W and three thicknesses of 2, 4, and 6 mm for cantaloupe drying, and the weight variations were determined. Artificial neural networks (ANN) and adaptive neuro-fuzzy inference systems (ANFIS) were exploited to investigate energy and exergy indices of cantaloupe drying using various afore-mentioned input parameters. The results indicated that a rise in microwave power and a decline in sample thickness can significantly decrease the specific energy consumption (SEC), energy loss, exergy loss, and improvement potential (probability level of 5%). The mean SEC, energy efficiency, energy loss, thermal efficiency, dryer efficiency, exergy efficiency, exergy loss, improvement potential, and sustainability index ranged in 10.48–25.92 MJ/kg water, 16.11–47.24%, 2.65–11.24 MJ/kg water, 7.02–36.46%, 12.36–42.70%, 11.25–38.89%, 3–12.2 MJ/kg water, 1.88–10.83 MJ/kg water, and 1.12–1.63, respectively. Based on the results, the use of higher microwave powers for drying thinner samples can improve the thermodynamic performance of the process. The ANFIS model offers a more accurate forecast of energy and exergy indices of cantaloupe drying compare to ANN model. [ABSTRACT FROM AUTHOR]

(1) Background: Forecasting of energy consumption demand is a crucial task linked directly with the economy of every country all over the world. Accurate natural gas consumption forecasting allows policy makers to formulate natural gas supply planning and apply the right strategic policies in this direction. In order to develop a real accurate natural gas (NG) prediction model for Greece, we examine the application of neuro-fuzzy models, which have recently shown significant contribution in the energy domain. (2) Methods: The adaptive neuro-fuzzy inference system (ANFIS) is a flexible and easy to use modeling method in the area of soft computing, integrating both neural networks and fuzzy logic principles. The present study aims to develop a proper ANFIS architecture for time series modeling and prediction of day-ahead natural gas demand. (3) Results: An efficient and fast ANFIS architecture is built based on neuro-fuzzy exploration performance for energy demand prediction using historical data of natural gas consumption, achieving a high prediction accuracy. The best performing ANFIS method is also compared with other well-known artificial neural networks (ANNs), soft computing methods such as fuzzy cognitive map (FCM) and their hybrid combination architectures for natural gas prediction, reported in the literature, to further assess its prediction performance. The conducted analysis reveals that the mean absolute percentage error (MAPE) of the proposed ANFIS architecture results is less than 20% in almost all the examined Greek cities, outperforming ANNs, FCMs and their hybrid combination; and (4) Conclusions: The produced results reveal an improved prediction efficacy of the proposed ANFIS-based approach for the examined natural gas case study in Greece, thus providing a fast and efficient tool for utterly accurate predictions of future short-term natural gas demand. [ABSTRACT FROM AUTHOR]

The estimation of solar radiation for planning current and future periods in different fields, such as renewable energy generation, is very important for decision makers. The current study presents a hybrid model structure based on a multi-objective shark algorithm and fuzzy method for forecasting and generating a zone map for solar radiation as an alternative solution for future renewable energy production. The multi-objective shark algorithm attempts to select the best input combination for solar radiation (SR) estimation and the optimal value of the adaptive neuro-fuzzy inference system (ANFIS) parameter, and the power parameter of the inverse distance weight (IDW) is computed. Three provinces in Iran with different climates and air quality index conditions have been considered as case studies for this research. In addition, comparative analysis has been carried out with other models, including multi-objective genetic algorithm-ANFIS and multi-objective particle swarm optimization-ANFIS. The Taguchi model is used to obtain the best value of random parameters for multi-objective algorithms. The comparison of the results shows that the relative deviation index (RDI) of the distributed solutions in the Pareto front based multi-objective shark algorithm has the lowest value in the spread index, spacing metric index, favorable distribution, and good diversity. The generated Pareto solutions based on the multi-objective shark algorithm are compared to those based on the genetic algorithm and particle swarm algorithm and found to be the optimal and near ideal solutions. In addition, the determination of the best solution based on a multi-criteria decision model enables the best input to the model to be selected based on different effective parameters. Three different performance indices have been used in this study, including the root mean square error, Nash–Sutcliffe efficiency, and mean absolute error. The generated zone map based on the multi-objective shark algorithm-ANFIS highly matches with the observed data in all zones in all case studies. Additionally, the analysis shows that the air quality index (AQI) should be considered as effective input for SR estimation. Finally, the measurement and analysis of the uncertainty based on the multi-objective shark algorithm-ANFIS were carried out. As a result, the proposed new hybrid model is highly suitable for the generation of accurate zone mapping for different renewable energy generation fields. In addition, the proposed hybrid model showed outstanding performance for the development of a forecasting model for the solar radiation value, which is essential for the decision-makers to draw a future plan for generating renewable energy based solar radiation. [ABSTRACT FROM AUTHOR]

This study presents the strategy of controlling the air discharge in the prototype of small scale compressed air energy storage (SS-CAES) to produce a constant voltage according to the user set point. The purpose of this study is to simplify the control of the SS-CAES, so that it can be integrated with a grid based on a constant voltage reference. The control strategy in this study is carried out by controlling the opening of the air valve combined with a servo motor using three intelligence control systems (fuzzy logic, artificial neural network (ANN), and adaptive neuro-fuzzy inference system (ANFIS)). The testing scenario of this system will be carried out using two scenes, including changing the voltage set point and by switching the load. The results that were obtained indicate that ANN has the best results, with an average settling time of 2.05S in the first test scenario and 6.65S in the second test scenario. [ABSTRACT FROM AUTHOR]