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Wind energy produced in near and offshore farms is constantly increasing, mainly thanks to the use of new technologies and the economic costs decrease. Data coming from satellites recently launched can be effectively used in near and offshore areas for wind speed mapping, and long and short-term analyses of wind regimes. Furthermore, the use of artificial intelligence methods is constantly increasing for predicting wind energy production. In this framework, a new forecasting model for wind speed assessment is presented integrating Sentinel satellite imagery analysis, in two phases using multi-sensor satellites, and machine learning methods. In the first step, wind speed and bathymetry have been analysed by means of sentinel-1 (S-1) and sentinel-2 (S-2) satellites images, respectively. Furthermore, a hybrid forecasting model has been proposed to assess and predict wind speed. The machine learning model consists of an integrated model using generalized regression neural network (GRNN) and the whale optimization algorithm (WOA). The new developed method has been then applied to assess wind energy potential around the Favignana island in Sicily, Italy. The results show that all the important primary parameters for a wind farm installations potential analysis, such as wind speed, water depth, and distance to the shoreline can be successfully analysed in a possible short time and freely. The following results have been obtained testing the new method: i) S-1 and S-2 satellite images have good potential for near and offshore wind speed assessment and bathymetry detection around Favignana island, ii) the root mean square error (RMSE), mean square error (MAE), and mean absolute percentage error (MAPE) of the proposed forecasting model using the whole dataset are 0.0205, 0.0159, and 6.8385 respectively, iii) the proposed forecasting model for wind speed has higher accuracy than other valid models.
M. Majidi Nezhad; A. Heydari; E. Pirshayan; D. Groppi; D. Astiaso Garcia. A novel forecasting model for wind speed assessment using sentinel family satellites images and machine learning method. Renewable Energy 2021, 179, 2198 -2211.
AMA StyleM. Majidi Nezhad, A. Heydari, E. Pirshayan, D. Groppi, D. Astiaso Garcia. A novel forecasting model for wind speed assessment using sentinel family satellites images and machine learning method. Renewable Energy. 2021; 179 ():2198-2211.
Chicago/Turabian StyleM. Majidi Nezhad; A. Heydari; E. Pirshayan; D. Groppi; D. Astiaso Garcia. 2021. "A novel forecasting model for wind speed assessment using sentinel family satellites images and machine learning method." Renewable Energy 179, no. : 2198-2211.
Wind turbines (WTs) are often operated in harsh and remote environments, thus making them more prone to faults and costly repairs. Additionally, the recent surge in wind farm installations have resulted in a dramatic increase in wind turbine data. Intelligent condition monitoring and fault warning systems are crucial to improving the efficiency and operation of wind farms and reducing maintenance costs. Gearbox is the major component that leads to turbine downtime. Its failures are mainly caused by the gearbox bearings. Devising condition monitoring approaches for the gearbox bearings is an effective predictive maintenance measure that can reduce downtime and cut maintenance cost. In this paper, we propose a hybrid intelligent condition monitoring and fault warning system for wind turbine’s gearbox. The proposed framework encompasses the following: a) clustering filter- (based on power, rotor speed, blade pitch angle, and wind speed signals)-using the automatic clustering model and ant bee colony optimization algorithm (ABC), b) prediction of gearbox bearing temperature and lubrication oil temperature signals- using variational mode decomposition (VMD), group method of data handling (GMDH) network, and multi-verse optimization (MVO) algorithm, and c) anomaly detection based on the Mahalanobis distances and wavelet transform denoising approach. The proposed condition monitoring system was evaluated using 10 min average SCADA datasets of two 2 MW on-shore wind turbines located in the south of Sweden. The results showed that this strategy can diagnose potential anomalies prior to failure and inhibit reporting alarms in healthy operations.
Azim Heydari; Davide Astiaso Garcia; Afef Fekih; Farshid Keynia; Lina Bertling Tjernberg; Livio De Santoli. A Hybrid Intelligent Model for the Condition Monitoring and Diagnostics of Wind Turbines Gearbox. IEEE Access 2021, 9, 1 -1.
AMA StyleAzim Heydari, Davide Astiaso Garcia, Afef Fekih, Farshid Keynia, Lina Bertling Tjernberg, Livio De Santoli. A Hybrid Intelligent Model for the Condition Monitoring and Diagnostics of Wind Turbines Gearbox. IEEE Access. 2021; 9 ():1-1.
Chicago/Turabian StyleAzim Heydari; Davide Astiaso Garcia; Afef Fekih; Farshid Keynia; Lina Bertling Tjernberg; Livio De Santoli. 2021. "A Hybrid Intelligent Model for the Condition Monitoring and Diagnostics of Wind Turbines Gearbox." IEEE Access 9, no. : 1-1.
A cost-effective and efficient wind energy production trend leads to larger wind turbine generators and drive for more advanced forecast models to increase their accuracy. This paper proposes a combined forecasting model that consists of empirical mode decomposition, fuzzy group method of data handling neural network, and grey wolf optimization algorithm. A combined K-means and identifying density-based local outliers is applied to detect and clean the outliers of the raw supervisory control and data acquisition data in the proposed forecasting model. Moreover, the empirical mode decomposition is employed to decompose signals and pre-processing data. The fuzzy GMDH neural network is a forecaster engine to estimate the future amount of wind turbines energy production, where the grey wolf optimization is used to optimize the fuzzy GMDH neural network parameters in order to achieve a lower forecasting error. Moreover, the model has been applied using actual data from a pilot onshore wind farm in Sweden. The obtained results indicate that the proposed model has a higher accuracy than others in the literature and provides single and combined forecasting models in different time-steps ahead and seasons.
Azim Heydari; Meysam Majidi Nezhad; Mehdi Neshat; Davide Garcia; Farshid Keynia; Livio De Santoli; Lina Bertling Tjernberg. A Combined Fuzzy GMDH Neural Network and Grey Wolf Optimization Application for Wind Turbine Power Production Forecasting Considering SCADA Data. Energies 2021, 14, 3459 .
AMA StyleAzim Heydari, Meysam Majidi Nezhad, Mehdi Neshat, Davide Garcia, Farshid Keynia, Livio De Santoli, Lina Bertling Tjernberg. A Combined Fuzzy GMDH Neural Network and Grey Wolf Optimization Application for Wind Turbine Power Production Forecasting Considering SCADA Data. Energies. 2021; 14 (12):3459.
Chicago/Turabian StyleAzim Heydari; Meysam Majidi Nezhad; Mehdi Neshat; Davide Garcia; Farshid Keynia; Livio De Santoli; Lina Bertling Tjernberg. 2021. "A Combined Fuzzy GMDH Neural Network and Grey Wolf Optimization Application for Wind Turbine Power Production Forecasting Considering SCADA Data." Energies 14, no. 12: 3459.
Short-term wind power prediction is challenging due to the chaotic characteristics of wind speed. Since, for wind power industries, designing an accurate and reliable wind power forecasting model is essential, we deployed a novel composite deep learning-based evolutionary approach for accurate forecasting of the power output in wind-turbine farms, which is developed in three stages. At the beginning stage (pre-processing), the k-means clustering method and an autoencoder are employed to detect and filter noise in the SCADA measurements. In the Next step (decomposition), in order to decompose the SCADA time-series data, we proposed a new hybrid variational mode decomposition (HVMD) method, that consists of VMD and two heuristics: greedy Nelder-Mead search algorithm (GNM) and adaptive randomised local search (ARLS). Both heuristics are applied to tune the hyper-parameters of VMD that results in improving the performance of the forecasting model. In the third phase, based on prior knowledge that the underlying wind patterns are highly non-linear and diverse, we proposed a novel alternating optimisation algorithm that consists of self-adaptive differential evolution (SaDE) algorithm and sine cosine optimisation method as a hyper-parameter optimizer and then combine with a recurrent neural network (RNN) called Long Short-term memory (LSTM). This framework allows us to model the power curve of a wind turbine on a farm. A historical dataset from supervisory control and data acquisition (SCADA) systems were applied as input to estimate the power output from an onshore wind farm in Sweden. Two short time forecasting horizons, including 10 min ahead and 1 h ahead, are considered in our experiments. The achieved prediction results supported the superiority of the proposed hybrid model in terms of accurate forecasting and computational runtime compared with earlier published hybrid models applied in this paper.
Mehdi Neshat; Meysam Majidi Nezhad; Ehsan Abbasnejad; SeyedAli Mirjalili; Daniele Groppi; Azim Heydari; Lina Bertling Tjernberg; Davide Astiaso Garcia; Bradley Alexander; Qinfeng Shi; Markus Wagner. Wind turbine power output prediction using a new hybrid neuro-evolutionary method. Energy 2021, 229, 120617 .
AMA StyleMehdi Neshat, Meysam Majidi Nezhad, Ehsan Abbasnejad, SeyedAli Mirjalili, Daniele Groppi, Azim Heydari, Lina Bertling Tjernberg, Davide Astiaso Garcia, Bradley Alexander, Qinfeng Shi, Markus Wagner. Wind turbine power output prediction using a new hybrid neuro-evolutionary method. Energy. 2021; 229 ():120617.
Chicago/Turabian StyleMehdi Neshat; Meysam Majidi Nezhad; Ehsan Abbasnejad; SeyedAli Mirjalili; Daniele Groppi; Azim Heydari; Lina Bertling Tjernberg; Davide Astiaso Garcia; Bradley Alexander; Qinfeng Shi; Markus Wagner. 2021. "Wind turbine power output prediction using a new hybrid neuro-evolutionary method." Energy 229, no. : 120617.
Air pollution monitoring is constantly increasing, giving more and more attention to its consequences on human health. Since Nitrogen dioxide (NO2) and sulfur dioxide (SO2) are the major pollutants, various models have been developed on predicting their potential damages. Nevertheless, providing precise predictions is almost impossible. In this study, a new hybrid intelligent model based on long short-term memory (LSTM) and multi-verse optimization algorithm (MVO) has been developed to predict and analysis the air pollution obtained from Combined Cycle Power Plants. In the proposed model, long short-term memory model is a forecaster engine to predict the amount of produced NO2 and SO2 by the Combined Cycle Power Plant, where the MVO algorithm is used to optimize the LSTM parameters in order to achieve a lower forecasting error. In addition, in order to evaluate the proposed model performance, the model has been applied using real data from a Combined Cycle Power Plant in Kerman, Iran. The datasets include wind speed, air temperature, NO2, and SO2 for five months (May–September 2019) with a time step of 3-h. In addition, the model has been tested based on two different types of input parameters: type (1) includes wind speed, air temperature, and different lagged values of the output variables (NO2 and SO2); type (2) includes just lagged values of the output variables (NO2 and SO2). The obtained results show that the proposed model has higher accuracy than other combined forecasting benchmark models (ENN-PSO, ENN-MVO, and LSTM-PSO) considering different network input variables. Graphic abstract
Azim Heydari; Meysam Majidi Nezhad; Davide Astiaso Garcia; Farshid Keynia; Livio De Santoli. Air pollution forecasting application based on deep learning model and optimization algorithm. Clean Technologies and Environmental Policy 2021, 1 -15.
AMA StyleAzim Heydari, Meysam Majidi Nezhad, Davide Astiaso Garcia, Farshid Keynia, Livio De Santoli. Air pollution forecasting application based on deep learning model and optimization algorithm. Clean Technologies and Environmental Policy. 2021; ():1-15.
Chicago/Turabian StyleAzim Heydari; Meysam Majidi Nezhad; Davide Astiaso Garcia; Farshid Keynia; Livio De Santoli. 2021. "Air pollution forecasting application based on deep learning model and optimization algorithm." Clean Technologies and Environmental Policy , no. : 1-15.
Nowadays, renewable energies are important sources for supplying electric power demand and a key entity of future energy markets. Therefore, wind power producers (WPPs) in most of the power systems in the world have a key role. On the other hand, the wind speed uncertainty makes WPPs deferent power generators, which in turn causes adequate bidding strategies, that leads to market rules, and the functional abilities of the turbines to penetrate the market. In this paper, a new bidding strategy has been proposed based on optimal scenario making for WPPs in a competitive power market. As known, the WPP generation is uncertain, and different scenarios must be created for wind power production. Therefore, a prediction intervals method has been improved in making scenarios and increase the accuracy of the presence of WPPs in the balancing market. Besides, a new optimization algorithm has been proposed called the grasshopper optimization algorithm to simulate the optimal bidding problem of WPPs. A set of numerical examples, as well as a case-study based on real-world data, allows illustrating and discussing the properties of the proposed method.
Azim Heydari; Gholamreza Memarzadeh; Davide Astiaso Garcia; Farshid Keynia; Livio De Santoli. Interval prediction algorithm and optimal scenario making model for wind power producers bidding strategy. Optimization and Engineering 2021, 1 -23.
AMA StyleAzim Heydari, Gholamreza Memarzadeh, Davide Astiaso Garcia, Farshid Keynia, Livio De Santoli. Interval prediction algorithm and optimal scenario making model for wind power producers bidding strategy. Optimization and Engineering. 2021; ():1-23.
Chicago/Turabian StyleAzim Heydari; Gholamreza Memarzadeh; Davide Astiaso Garcia; Farshid Keynia; Livio De Santoli. 2021. "Interval prediction algorithm and optimal scenario making model for wind power producers bidding strategy." Optimization and Engineering , no. : 1-23.
Offshore Wind (OW) speed assessment is a key aspect for the development of new wind farms at sea. Satellites can be used to globally obtain ocean and sea distribution, compensating limited in-situ measurements. In this study, a new methodology to estimate the wind’s speed potential is here proposed. Preliminary, Sentinel-1 (S-1) images have been analyzed by means of the Sentinel Application Platform (SNAP) software, extrapolating wind speed data for each cell pixel size of a testing area. Then GIS (Geographic Information System) software has been used to map wind data and find the best pixel location comparing these data with in-situ data. Furthermore, wind speed has been analyzed using the ERA-Interim reanalysis dataset for areas within 11 km and 40 km from the Lillgrund OW farm in the Baltic Sea to better understand wind regimes. Finally, wind speed parameters obtained by S-1 in Sea Surface Water (SSW) with the 10 m standard high have been compared with wind speed recorded by Supervisory Control and Data Acquisition (SCADA) systems of two turbine using wind profile formula. Obtained results show the comparison accuracy of wind speed assessment for each center of the pixels by S-1 satellite images and in-situ (SCADA) measurements. Data actually depends on the distance between the selected center pixel and the location of the turbines. The obtained wind speed differences (0.26 m/s - RMSE = 1.38 and 0.92 m/s - RMSE = 1.82) pinpointed the direct effect of the distance between the selected pixel center and the in-situ measurements location in the S-1 imagery for wind speed potential assessment. Obtained results proved an improvement of the OW assessment accuracy using multiple satellite observations, demonstrating that SAR wind maps can support OW speed sites assessment by introducing observations in different phases of an OW farm project.
M. Majidi Nezhad; M. Neshat; A. Heydari; A. Razmjoo; G. Piras; D. Astiaso Garcia. A new methodology for offshore wind speed assessment integrating Sentinel-1, ERA-Interim and in-situ measurement. Renewable Energy 2021, 172, 1301 -1313.
AMA StyleM. Majidi Nezhad, M. Neshat, A. Heydari, A. Razmjoo, G. Piras, D. Astiaso Garcia. A new methodology for offshore wind speed assessment integrating Sentinel-1, ERA-Interim and in-situ measurement. Renewable Energy. 2021; 172 ():1301-1313.
Chicago/Turabian StyleM. Majidi Nezhad; M. Neshat; A. Heydari; A. Razmjoo; G. Piras; D. Astiaso Garcia. 2021. "A new methodology for offshore wind speed assessment integrating Sentinel-1, ERA-Interim and in-situ measurement." Renewable Energy 172, no. : 1301-1313.
The correct strategy for monitoring and assessing marine Renewable Energy Sources (RESs) is of great importance for local/national sustainable development. To achieve this goal, it is necessary to measure in the most precise possible manner the local/regional RESs potential. This is especially true for Offshore Wind (OW) energy potential, since the most precise techniques are long and expensive, and are not able to assess the RESs potential of large areas. Today, Remote Sensing (RS) satellites can be considered the most important land and marine observation tools. The RS tools can be used to identify the interested areas for future OW energy converters installations in large and small-scale areas. In this study, the OW energy potential has been analysed by means of a 40 years wind speed data from the European Centre for Medium-Range Weather Forecasts (ECMWF) reanalysis dataset of the Samothraki island surrounding area in the Mediterranean Sea. The OW speed potential has been analysed by means of monthly data from ECMWF Interim reanalysis (ERA-Interim) datasets using the Network Common Data Form (NetCDF) format. Automatically, analyses have been carried out using the Region Of Interest (ROI) tool and Geographical Information System (GIS) software in order to extract information about the OW speed assessment of the Samothraki island area. The primary results of this study show that the southwest area of Samothraki island has good potential for future OW farms installation (bottom fixed and floating version) in near and offshore areas. This study shows the OW energy potential per location, as well as the trend of OW speed, which has changed over the past 40 years in the Mediterranean Sea.
M. Majidi Nezhad; M. Neshat; D. Groppi; P. Marzialetti; A. Heydari; G. Sylaios; D. Astiaso Garcia. A primary offshore wind farm site assessment using reanalysis data: a case study for Samothraki island. Renewable Energy 2021, 172, 667 -679.
AMA StyleM. Majidi Nezhad, M. Neshat, D. Groppi, P. Marzialetti, A. Heydari, G. Sylaios, D. Astiaso Garcia. A primary offshore wind farm site assessment using reanalysis data: a case study for Samothraki island. Renewable Energy. 2021; 172 ():667-679.
Chicago/Turabian StyleM. Majidi Nezhad; M. Neshat; D. Groppi; P. Marzialetti; A. Heydari; G. Sylaios; D. Astiaso Garcia. 2021. "A primary offshore wind farm site assessment using reanalysis data: a case study for Samothraki island." Renewable Energy 172, no. : 667-679.
The elaboration of a methodology for accurately assessing the potentialities of blue renewable energy sources is a key challenge among the current energy sustainability strategies all over the world. Consequentially, many researchers are currently working to improve the accuracy of marine renewable assessment methods. Nowadays, remote sensing (RSs) satellites are used to observe the environment in many fields and applications. These could also be used to identify regions of interest for future energy converter installations and to accurately identify areas with interesting potentials. Therefore, researchers can dramatically reduce the possibility of significant error. In this paper, a comprehensive SWOT (strengths, weaknesses, opportunities and threats) analysis is elaborated to assess RS satellite potentialities for offshore wind (OW) estimation. Sicily and Sardinia—the two biggest Italian islands with the highest potential for offshore wind energy generation—were selected as pilot areas. Since there is a lack of measuring instruments, such as cup anemometers and buoys in these areas (mainly due to their high economic costs), an accurate analysis was carried out to assess the marine energy potential from offshore wind. Since there are only limited options for further expanding the measurement over large areas, the use of satellites makes it easier to overcome this limitation. Undoubtedly, with the advent of new technologies for measuring renewable energy sources (RESs), there could be a significant energy transition in this area that requires a proper orientation of plans to examine the factors influencing these new technologies that can negatively affect most of the available potential. Satellite technology for identifying suitable areas of wind power plants could be a powerful tool that is constantly increasing in its applications but requires good planning to apply it in various projects. Proper planning is only possible with a better understanding of satellite capabilities and different methods for measuring available wind resources. To this end, a better understanding in interdisciplinary fields with the exchange of updated information between different sectors of development, such as universities and companies, will be most effective. In this context, by reviewing the available satellite technologies, the ability of this tool to measure the marine renewable energies (MREs) sector in large and small areas is considered. Secondly, an attempt is made to identify the strengths and weaknesses of using these types of tools and techniques that can help in various projects. Lastly, specific scenarios related to the application of such systems in existing and new developments are reviewed and discussed.
Meysam Majidi Nezhad; Riyaaz Uddien Shaik; Azim Heydari; Armin Razmjoo; Niyazi Arslan; Davide Astiaso Garcia. A SWOT Analysis for Offshore Wind Energy Assessment Using Remote-Sensing Potential. Applied Sciences 2020, 10, 6398 .
AMA StyleMeysam Majidi Nezhad, Riyaaz Uddien Shaik, Azim Heydari, Armin Razmjoo, Niyazi Arslan, Davide Astiaso Garcia. A SWOT Analysis for Offshore Wind Energy Assessment Using Remote-Sensing Potential. Applied Sciences. 2020; 10 (18):6398.
Chicago/Turabian StyleMeysam Majidi Nezhad; Riyaaz Uddien Shaik; Azim Heydari; Armin Razmjoo; Niyazi Arslan; Davide Astiaso Garcia. 2020. "A SWOT Analysis for Offshore Wind Energy Assessment Using Remote-Sensing Potential." Applied Sciences 10, no. 18: 6398.
Electricity price forecasting is a key aspect for market participants to maximize their economic efficiency in deregulated markets. Nevertheless, due to its non-linearity and non-stationarity, the trend of the price is usually complicated to predict. On the other hand, the accuracy of short-term electricity price and load forecasting is fundamental for an efficient management of electric systems. An accurate prediction can benefit future plans and economic operations of the power systems’ operators. In this paper, a new and accurate combined model has been proposed for short-term load forecasting and short-term price forecasting in deregulated power markets. It includes variational mode decomposition, mix data modeling, feature selection, generalized regression neural network and gravitational search algorithm. A mixed data model for the price and load forecast has been considered and integrated with the original signal series of price and load and their decomposition. Throughout this model, the candidate input variables are chosen by a distinct hybrid feature selection. Two reliable electricity markets (Pennsylvania-New Jersey-Maryland and Spanish electricity markets) have been used to test the proposed forecasting model and the obtained results have been compared with different valid benchmark prediction models. Lastly, the real load data of Favignana Island's power grid have been considered to test the proposed model. The obtained results pinpointed that the proposed model’s precision and stability is higher than in other benchmark forecasting models.
Azim Heydari; Meysam Majidi Nezhad; Elmira Pirshayan; Davide Astiaso Garcia; Farshid Keynia; Livio De Santoli. Short-term electricity price and load forecasting in isolated power grids based on composite neural network and gravitational search optimization algorithm. Applied Energy 2020, 277, 115503 .
AMA StyleAzim Heydari, Meysam Majidi Nezhad, Elmira Pirshayan, Davide Astiaso Garcia, Farshid Keynia, Livio De Santoli. Short-term electricity price and load forecasting in isolated power grids based on composite neural network and gravitational search optimization algorithm. Applied Energy. 2020; 277 ():115503.
Chicago/Turabian StyleAzim Heydari; Meysam Majidi Nezhad; Elmira Pirshayan; Davide Astiaso Garcia; Farshid Keynia; Livio De Santoli. 2020. "Short-term electricity price and load forecasting in isolated power grids based on composite neural network and gravitational search optimization algorithm." Applied Energy 277, no. : 115503.
With the increasing number of population and the rising demand for electricity, providing safe and secure energy to consumers is getting more and more important. Adding dispersed products to the distribution network is one of the key factors in achieving this goal. However, factors such as the amount of investment and the return on the investment on one side, and the power grid conditions, such as loss rates, voltage profiles, reliability, and maintenance costs, on the other hand, make it more vital to provide optimal annual planning methods concerning network development. Accordingly, in this paper, a multilevel method is presented for optimal network power expansion planning based on the binary dragonfly optimization algorithm, taking into account the distributed generation. The proposed objective function involves the minimization of the cost of investment, operation, repair, and the cost of reliability for the development of the network. The effectiveness of the proposed model to solve the multiyear network expansion planning problem is illustrated by applying them on the 33-bus distribution network and comparing the acquired results with the results of other solution methods such as GA, PSO, and TS.
Mohammad Kakueinejad; Azim Heydari; Mostafa Askari; Farshid Keynia. Optimal Planning for the Development of Power System in Respect to Distributed Generations Based on the Binary Dragonfly Algorithm. Applied Sciences 2020, 10, 4795 .
AMA StyleMohammad Kakueinejad, Azim Heydari, Mostafa Askari, Farshid Keynia. Optimal Planning for the Development of Power System in Respect to Distributed Generations Based on the Binary Dragonfly Algorithm. Applied Sciences. 2020; 10 (14):4795.
Chicago/Turabian StyleMohammad Kakueinejad; Azim Heydari; Mostafa Askari; Farshid Keynia. 2020. "Optimal Planning for the Development of Power System in Respect to Distributed Generations Based on the Binary Dragonfly Algorithm." Applied Sciences 10, no. 14: 4795.
Mediterranean islands have the advantage of favourable climatic conditions to use different marine renewable energy sources. Remote sensing can provide data to determine wind energy production potential and observational activity to identify, assess and detect suitable points in large marine areas. In this paper, a new combined model has been developed to integrate wind speed assessment, mapping and forecasting using Sentinel 1 satellite data through images processing and Adaptive Neuro-Fuzzy Inference System and the Bat algorithm. Synthetic Aperture Radar (SAR) satellite images from the Sentinel 1 satellite have been used in order to detect offshore and nearshore wind potential. Particularly, Sentinel 1 images have been analysed by means of the SNAP software. Then, to extract data about wind speed and direction, a GIS software for mapping the wind climate has been used. This new methodology has been applied to the North-Central coasts of Sardinia Island and then focused on six main small islands of La Maddalena archipelago. Furthermore, ten Hot Spots (HSs) have been identified as interesting because of their high-energy potential and the possibility to be considered as sites for future implementation of Wind Turbine Generators (WTGs). Finally, the ten identified HS have been used as input data to train and test the proposed forecast model.
M. Majidi Nezhad; A. Heydari; D. Groppi; F. Cumo; D. Astiaso Garcia. Wind source potential assessment using Sentinel 1 satellite and a new forecasting model based on machine learning: A case study Sardinia islands. Renewable Energy 2020, 155, 212 -224.
AMA StyleM. Majidi Nezhad, A. Heydari, D. Groppi, F. Cumo, D. Astiaso Garcia. Wind source potential assessment using Sentinel 1 satellite and a new forecasting model based on machine learning: A case study Sardinia islands. Renewable Energy. 2020; 155 ():212-224.
Chicago/Turabian StyleM. Majidi Nezhad; A. Heydari; D. Groppi; F. Cumo; D. Astiaso Garcia. 2020. "Wind source potential assessment using Sentinel 1 satellite and a new forecasting model based on machine learning: A case study Sardinia islands." Renewable Energy 155, no. : 212-224.
This paper proposes a novel hybrid strategy based on intelligent approaches to forecast electricity consumptions. The proposed forecasting strategy includes three main steps: (a) the evaluation of a correlation coefficient for socio-economic indicators on electric energy consumptions, (b) the classification of historical and socio-economic indicators using the proposed feature selection method, (c) the development of a new combined method using Adaptive Neuro-Fuzzy Inference System and Whale Optimization Algorithm to predict electrical energy consumptions. The simulation results have been tested and validated by real data sets achieved within 1992 and 2010 in two pilot cases in a developing country (Iran) and a developed one (Italy). The research findings pinpointed the greater accuracy and stability of the new developed method for electrical energy consumption forecasting compared to existing single and hybrid benchmark models.
Azim Heydari; Davide Astiaso Garcia; Farshid Keynia; Fabio Bisegna; Livio De Santoli. Hybrid intelligent strategy for multifactor influenced electrical energy consumption forecasting. Energy Sources, Part B: Economics, Planning, and Policy 2019, 14, 341 -358.
AMA StyleAzim Heydari, Davide Astiaso Garcia, Farshid Keynia, Fabio Bisegna, Livio De Santoli. Hybrid intelligent strategy for multifactor influenced electrical energy consumption forecasting. Energy Sources, Part B: Economics, Planning, and Policy. 2019; 14 (10-12):341-358.
Chicago/Turabian StyleAzim Heydari; Davide Astiaso Garcia; Farshid Keynia; Fabio Bisegna; Livio De Santoli. 2019. "Hybrid intelligent strategy for multifactor influenced electrical energy consumption forecasting." Energy Sources, Part B: Economics, Planning, and Policy 14, no. 10-12: 341-358.
Nowadays, wind and solar power generation have a major impact in many microgrid hybrid energy systems based on their cost and pollution. On the other hand, accurate forecasting of wind and solar power generation is very important for energy management in microgrids. Therefore, a novel prediction interval model, consisting of several sections (wavelet transform, hybrid feature selection, Group Method of Data Handling neural network, and modified multi-objective fruit fly optimization algorithm), has been developed to short-term predict wind speed and solar irradiation and to investigate the energy consumption of microgrids. The renewables prediction and the energy consumption analysis have been applied to the Favignana island microgrid, in the south of Italy, using the new proposed point forecast model (Group Method of Data Handling neural network and modified fruit fly optimization algorithm – GMDHMFOA) and a Pareto analysis. The results show that the proposed interval prediction model has a good performance in different confidence levels (95%, 90%, and 85%) to predict wind speed and solar irradiation than other already existing methods. In addition, the proposed point forecast model (GMDHMFOA) has an acceptable error and better performance than the other ones commonly used in predicting energy consumption. Lastly, the monthly energy consumption in different stations of the microgrid can be predicted by using the proposed model and provides suitable solutions for energy management of the microgrid.
Azim Heydari; Davide Astiaso Garcia; Farshid Keynia; Fabio Bisegna; Livio De Santoli. A novel composite neural network based method for wind and solar power forecasting in microgrids. Applied Energy 2019, 251, 113353 .
AMA StyleAzim Heydari, Davide Astiaso Garcia, Farshid Keynia, Fabio Bisegna, Livio De Santoli. A novel composite neural network based method for wind and solar power forecasting in microgrids. Applied Energy. 2019; 251 ():113353.
Chicago/Turabian StyleAzim Heydari; Davide Astiaso Garcia; Farshid Keynia; Fabio Bisegna; Livio De Santoli. 2019. "A novel composite neural network based method for wind and solar power forecasting in microgrids." Applied Energy 251, no. : 113353.
Reducing CO2 emissions is a key goal of the strategy for a low-carbon economy and for the choice of greenhouse gas emission mitigation path. An effective forecasting method can represent a useful tool for managing renewable energies in microgrids and mitigating carbon dioxide emission. In this study is evaluated the trend of CO2 emission in Iran, Canada and Italy and compared the CO2 emission from consumption of energy sources: Coal - Natural Gas - Petroleum and other refined hydrocarbons – Renewable Energies. Furthermore, a proposed intelligent method has been provided for CO2 emission forecasting based on Generalized Regression Neural Network and Grey Wolf Optimization. Furthermore, the proposed method has been used for renewable energies generation (Wind power and Solar power) forecasting in the microgrid of Favignana island (Italy). The obtained results confirm the higher accuracy of the proposed method in long-term CO2 emission forecasting and short-term renewable energies generation as compared with other several methods.
Azim Heydari; Davide Astiaso Garcia; Farshid Keynia; Fabio Bisegna; Livio De Santoli. Renewable Energies Generation and Carbon Dioxide Emission Forecasting in Microgrids and National Grids using GRNN-GWO Methodology. Energy Procedia 2019, 159, 154 -159.
AMA StyleAzim Heydari, Davide Astiaso Garcia, Farshid Keynia, Fabio Bisegna, Livio De Santoli. Renewable Energies Generation and Carbon Dioxide Emission Forecasting in Microgrids and National Grids using GRNN-GWO Methodology. Energy Procedia. 2019; 159 ():154-159.
Chicago/Turabian StyleAzim Heydari; Davide Astiaso Garcia; Farshid Keynia; Fabio Bisegna; Livio De Santoli. 2019. "Renewable Energies Generation and Carbon Dioxide Emission Forecasting in Microgrids and National Grids using GRNN-GWO Methodology." Energy Procedia 159, no. : 154-159.
Farshid Keynia; Azim Heydari. A new short-term energy price forecasting method based on wavelet neural network. International Journal of Mathematics in Operational Research 2019, 14, 1 .
AMA StyleFarshid Keynia, Azim Heydari. A new short-term energy price forecasting method based on wavelet neural network. International Journal of Mathematics in Operational Research. 2019; 14 (1):1.
Chicago/Turabian StyleFarshid Keynia; Azim Heydari. 2019. "A new short-term energy price forecasting method based on wavelet neural network." International Journal of Mathematics in Operational Research 14, no. 1: 1.
A wavelet neural network (WNN) is proposed for short-term price forecasting (STPF) in electricity markets. Back propagation algorithm is used for training the wavelet neural network for prediction. Weights in the back propagation algorithm are usually initialised with small random values. If the random initial weights happen to be far from a suitable solution or near a poor local optimum, training may take a long time or get trapped in the local optimum. In this paper, we show that WNN has acceptable prediction properties compared to other forecasting techniques. We investigated proper weight initialisations of WNN, and proved that it attains a superior prediction performance. Finally, we used a two-step correlation analysis algorithm for input selecting. This algorithm selects the best relevant and non-redundant input features for WNN. Our model is examined for MCP prediction of the Spanish market and LMP forecasting in PJM (Pennsylvania, New Jersey and Maryland) market for the year 2002 and 2006 respectively.
Farshid Keynia; Azim Heydari. A new short-term energy price forecasting method based on wavelet neural network. International Journal of Mathematics in Operational Research 2019, 14, 1 .
AMA StyleFarshid Keynia, Azim Heydari. A new short-term energy price forecasting method based on wavelet neural network. International Journal of Mathematics in Operational Research. 2019; 14 (1):1.
Chicago/Turabian StyleFarshid Keynia; Azim Heydari. 2019. "A new short-term energy price forecasting method based on wavelet neural network." International Journal of Mathematics in Operational Research 14, no. 1: 1.
The forecasting of electricity load is considered as an essential instrument, especially in countries with a restructured electricity market. The mid-term prediction is performed for the period within 1 month to 1 or 2 years and it is important for mid-term planning, including planning of repairs and economic exploitation of power systems, which are related to the reliability of the system directly. The forecast horizon in this paper is monthly and on a daily basis (peak load). The combined method of the neural network and the particle optimization algorithm were used to predict the load, and then the maximum amount of environmental pollution caused by the production of electricity required to supply the predicted load was calculated. The applied method was tested on the data of a North American electric company for four months (four seasons) and in comparison to the other methods presented in previous studies, it had an acceptable accuracy.
Azim Heydari; Farshid Keynia; Davide Astiaso Garcia; Livio De Santoli. Mid-Term Load Power Forecasting Considering Environment Emission using a Hybrid Intelligent Approach. 2018 5th International Symposium on Environment-Friendly Energies and Applications (EFEA) 2018, 1 -5.
AMA StyleAzim Heydari, Farshid Keynia, Davide Astiaso Garcia, Livio De Santoli. Mid-Term Load Power Forecasting Considering Environment Emission using a Hybrid Intelligent Approach. 2018 5th International Symposium on Environment-Friendly Energies and Applications (EFEA). 2018; ():1-5.
Chicago/Turabian StyleAzim Heydari; Farshid Keynia; Davide Astiaso Garcia; Livio De Santoli. 2018. "Mid-Term Load Power Forecasting Considering Environment Emission using a Hybrid Intelligent Approach." 2018 5th International Symposium on Environment-Friendly Energies and Applications (EFEA) , no. : 1-5.
Pistachio produce is of great importance in the world and considered as a valuable agricultural produce. This product is important for the economy of the producing countries as well as other importer countries. In this regard, countries which are capable of getting control over this product’s market would obtain considerable benefits; one way to do so is predicting market trend or the price of this product and the affecting factors. In this survey, initially, the affecting parameters on pistachio price are identified. Then, using a novel combined intelligent method, the price of this product is predicted. In addition, using analysis of variance method, the results of the proposed method are compared to other intelligent combined methods, such as Feed Forward-Particle Swarm Optimization, Elman- Imperialist Competitive Algorithm, Feed Forward-Imperialist Competitive Algorithm, Elman-Genetic Algorithm, and Feed Forward-Genetic Algorithm. The results of the mentioned comparison indicate that the proposed method owns an excellent performance.
Azim Heydari; Farshid Keynia; Nasser Shahsavari-Pour; Reza Sedaghat. An evolutionary hybrid method to predict pistachio price. Complex & Intelligent Systems 2017, 3, 121 -132.
AMA StyleAzim Heydari, Farshid Keynia, Nasser Shahsavari-Pour, Reza Sedaghat. An evolutionary hybrid method to predict pistachio price. Complex & Intelligent Systems. 2017; 3 (2):121-132.
Chicago/Turabian StyleAzim Heydari; Farshid Keynia; Nasser Shahsavari-Pour; Reza Sedaghat. 2017. "An evolutionary hybrid method to predict pistachio price." Complex & Intelligent Systems 3, no. 2: 121-132.
Ranking of fuzzy numbers plays a great role in decision-making problems. Fuzzy number comparison is required in decision analysis and fuzzy environment. One major part of fuzzy logic is ranking of the fuzzy numbers. In many fuzzy program systems, fuzzy numbers ranking has an important role in decision making and data analysis. This paper presents a novel method for fuzzy numbers ranking, which performs based on calculation of different parts of right and left of fuzzy numbers. The suggested method consists of simple calculations and is able to calculate all triangular fuzzy numbers (normal and non-normal) and trapezoidal fuzzy numbers (normal and non-normal). In the current study, due to employing analysis of variance method (ANOVA) to regulate the parameters and the approval of parameters' defined value, the results from the rating are guaranteed and have a high quality.
Nasser Shahsavari Pour; Azim Heydari; Mojtaba Kazemi; Mehdi Karami. A novel method for ranking fuzzy numbers based on the different areas fuzzy number. International Journal of Mathematics in Operational Research 2017, 11, 544 .
AMA StyleNasser Shahsavari Pour, Azim Heydari, Mojtaba Kazemi, Mehdi Karami. A novel method for ranking fuzzy numbers based on the different areas fuzzy number. International Journal of Mathematics in Operational Research. 2017; 11 (4):544.
Chicago/Turabian StyleNasser Shahsavari Pour; Azim Heydari; Mojtaba Kazemi; Mehdi Karami. 2017. "A novel method for ranking fuzzy numbers based on the different areas fuzzy number." International Journal of Mathematics in Operational Research 11, no. 4: 544.