Short-Term Wind Speed Forecasting Model Using Hybrid Neural Networks and Wavelet Packet Decomposition

Wind speed is one of the most vital, imperative meteorological parameters, thus the prediction of which is of fundamental importance in the studies related to energy management, building construction, damages caused by strong winds, aquatic needs of power plants, the prevalence and spread of diseases, snowmelt, and air pollution. Due to the discrete and nonlinear structure of wind speed, wind speed forecasting at regular intervals is a crucial problem. In this regard, a wide variety of prediction methods have been applied. So far, many activities have been done in order to make optimal use of renewable energy sources such as wind, which have led to the present diverse types of wind speed and strength measuring methods in the various geographical locations. In this paper, a novel forecasting model based on hybrid neural networks (HNNs) and wavelet packet decomposition (WPD) processor has been proposed to predict wind speed. Considering this scenario, the accuracy of the proposed method is compared with other wind speed prediction methods to ensure performance improvement

Interval prediction algorithm and optimal scenario making model for wind power producers bidding strategy

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

Reliability based maintenance programming by a new index for electrical distribution system components ranking

Reliability and accessibility of distribution systems are important goals that have significant impacts on the costs. The proper strategy of maintenance based on components arrangement and assets is the best way to reach these goals. This strategy is a kind of uses reliability-centered maintenance (RCM). Due to the limited maintenance budget, performing maintenance activities for all components of the system is neither possible nor logical. So most of the resources should be allocated to the most critical and important components. This paper presents a novel analytical method of prioritization of distribution systems’ components by introducing a new weighted cumulative Reliability-based diagnostic importance factor. This new factor includes different reliability indexes in form of diagnostic factors and will show that the order of components obtained by this method is better than another method in saving the budget and providing reliability of the system. The process of decision-making for prioritization of distribution systems’ components based on their criticality degree will both improve the reliability level of the total system and decrease the cost of load interruption and finally maintenance costs. The proposed model is implemented on a radial distribution network. Numerical results show the effectiveness of the proposed RCM model for micro-grids

Hybrid intelligent strategy for multifactor influenced electrical energy consumption forecasting

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

Air pollution forecasting application based on deep learning model and optimization algorithm

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: [Figure not available: see fulltext.

A Mediterranean sea offshore wind classification using MERRA-2 and machine learning models

This paper uses a Modern-Era Retrospective analysis for Research and Applications version 2 (MERRA-2) re-analysis to identify long-term Mediterranean Sea Offshore Wind (OW) classification possible locations. In particular, an OW classification based on the last 40-years period OW speeds highlighted the best areas for potential Offshore Wind Turbine Generators (OWTG) installations in the Mediterranean basin. Preliminary, long-term OW classification results show that several Mediterranean basin zones in the Aegean Sea, Gulf of Lyon, the Northern Morocco and Tunisia regions have attractive OW potential. Secondly, a combined forecasting model based on the wavelet decomposition method and long-term memory neural network has been developed to predict the short-term wind speed considering the last ten years of hourly data for Mediterranean areas. The results of the proposed model for wind speed prediction have been compared with other single models, Multilayer Perceptron (MLP) and Long Short-Term Memory (LSTM), highlighting a higher level of accuracy. Finally, three Weibull fitting algorithms have been provided to analyze the wind energy potential in the Mediterranean basin

Short-term electricity price and load forecasting in isolated power grids based on composite neural network and gravitational search optimization algorithm

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

A hybrid intelligent model for the condition monitoring and diagnostics of wind turbines gearbox

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

A combined multi-objective intelligent optimization approach considering techno-economic and reliability factors for hybrid-renewable microgrid systems

This paper proposes a novel optimization strategy for hybrid-renewable energy systems in microgrids. The multi-objective optimization approach is formulated for a PV-wind-diesel-battery hybrid system. Its main objectives are to minimize the levelized cost of energy (LCOE) and loss of power supply probability (LPSP) whilst maximizing the use of renewable energy sources (RES). The proposed optimization strategy combines the Taguchi method with a novel fuzzy decision-maker-based multi-objective optimization algorithm. It implements, a) an energy management strategy to optimize the use of the energy sources, b) a Taguchi method to determine the upper bounds of the model's decision variables, c) a multi-objective moth flame optimization algorithm to optimize the size of the renewable energy sources, and d) a fuzzy decision-making approach to obtain the best Pareto front. The proposed strategy was implemented to optimize the design of a hybrid renewable energy system based on three different scenarios consisting of 10, 15, and 20 residential houses located in Sønderborg, a town in the Region of Southern Denmark. The results of the proposed model in terms of loss of power supply probability and levelized cost of energy for the scenarios I, II, and III are [0.224, 0.754], [0.313, 0.612], and [0.368, 0.547] respectively. In addition, the optimal design of the hybrid renewable-based microgrid system for scenarios I, II, and III are [PV: 32 kW, AD: 4.86, WT: 6], [PV: 36 kW, AD: 4.73, WT: 7], and [PV: 47 kW, AD: 5, WT: 7], respectively. The effectiveness of the proposed multi-objective optimization algorithm (MOMFO) in solving the optimization problem was examined and the results were further compared with those of the Non-Dominated Sorting Algorithm II(NSGA-II), Multi-Objective Particle Swarm Optimization (MOPSO), and Multi-Objective Social Engineering Optimizer (MOSEO)

A combined fuzzy gmdh neural network and grey wolf optimization application for wind turbine power production forecasting considering scada data

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