Long Short‐Term Memory Wavelet Neural Network for Renewable Energy Generation Forecasting

In recent years, the use of renewable energy has grown significantly in electricity generation. However, the output of such facilities can be uncertain, affecting their reliability. The forecast of renewable energy production is necessary to guarantee the system’s stability. Several authors have already developed deep learning techniques and hybrid systems to make predictions as accurate as possible. However, the accurate forecasting of renewable energy still is a challenging task. This work proposes a new hybrid system for renewable energy forecasting that combines the traditional linear model (Seasonal Autoregressive Integrated Moving Average—SARIMA) with a state-of-the-art Machine Learning (ML) model, Transformer neural network, using exogenous data. The proposal, named H-Transformer, is compared with other hybrid systems and single ML models, such as Long Short Term Memory (LSTM), Gated Recurrent Unit (GRU), and Recurrent Neural Networks (RNN), using five data sets of wind speed and solar energy. The proposed H-Transformer attained the best result compared to all single models in all datasets and evaluation metrics. Finally, the hybrid H-Transformer obtained the best result in most cases when compared to other hybrid approaches, showing that the proposal can be a useful tool in renewable energy forecasting.

With the increase of the capacity of PV generated systems, how to eliminate the problem caused by the randomness of power output for photovoltaic system becomes more significant. Most of the existing photovoltaic prediction is Based on the solar radiation. However, it's difficult to implement in China due to insufficient solar radiation station available and poor forecasting performance. In addition, indirect forecasting cannot consider the factors related with PV system. A novel power forecasting model using historical power is proposed to solve the problems. Furthermore, in order to adapt sudden weather changes, the future weather type was recognized by using self-organizing feature map(SOM). Then, PV power generation in each weather type could be forecasted from its corresponding forecast network and the over fitting issue of single network model could be addressed. Wavelet neural network is combined with wavelet analysis and neural network. It is compatible with the good time-frequency property and good fault tolerant ability of neural network. Wavelet neural network can optimize the forecasting model. The experimental results indicate that the prediction has high precision and can be applied in stable operation of photovoltaic generation system.

The Sixth International Symposium on Neural Networks (ISNN 2009)

During the past years, the convergence of machine learning (ML) technologies with renewable energy sectors has become a significant key area of innovation as a key area of innovation, enhancing the efficiency and predictability of sustainable energy sources. ML algorithms, adept at handling complex data, have become essential in forecasting energy outputs from variable sources like solar and wind. This integration has led to the development of smarter, more adaptive grid systems, capable of efficiently managing the variability of renewable energy sources. This review paper focuses on several key areas: firstly, it provides a summary of related work, specifically focusing on ML in the renewable energy field. Secondly, it delves into ML models and evaluation metrics used for solar and wind energy forecasting. Thirdly, it analyzes 21 studies published from 2019 to 2023, primarily centered on solar energy (60%) and wind energy (40%), with an emphasis on various forecasting horizons, highlighting the results of the ML algorithms used and the performance metrics to evaluate their effectiveness. Finally, it identifies gaps and opportunities in this field. The state-of-the-art review and its findings can offer a solid foundation for future research initiatives.

Signal-devices management and data-driven evidential constraints based robust dispatch strategy of virtual power plant

Nonlinear dynamic systems are characterized with uncertainties in terms of structure and parameters. These uncertainties cannot be described by deterministic models. The modelling and identification of nonlinear dynamic systems through the measured experimental data is a problem in engineering and technical processes. Therefore, field of system identification have become an important area of research. Fuzzy technology is an effective tool for dealing with complex nonlinear processes that are characterized with uncertain factors. In this paper, a novel approach based on Local Linear method learning in dynamical filter weights neurons for the identification of non-linear dynamic systems is presented. The fuzzy wavelet neural network combines wavelet theory with fuzzy logic and neural networks. Learning fuzzy rules and parameter update in fuzzy wavelet neural network is based on gradient decent method. The proposed approach is said to be Fuzzy Local Linear Wavelet Neural Network based model. It has been explained through examples. The structure is tested for the identification with both wavelet neural network and Fuzzy Local Linear Wavelet Neural Network that shows the comparative performance.KeywordsSystem IdentificationNon-linear SystemWavelet Neural NetworkFuzzy Wavelet Neural NetworkLocal Linear Wavelet Neural NetworkFuzzy Local Linear Wavelet Neural Network

Wavelet transformation has the ability of representing a function and revealing the properties of the function in the joint local regions of the time frequency space. Based on wavelet and artificial neural network, a Wavelet Neural Network (WNN) model predicting the average post-dry out Nusselt number for upward flow in vertical narrow annuli with bilateral heating has been developed. The WNN mode combining the properties of the wavelet transform and the advantages of Artificial Neural Networks (ANN) has some advantages of solving non-linear problem.

In 2014, the relevant research data from the Ministry of Environmental Protection and the Ministry of Land and Resources showed that the total exceedance rate of soil heavy metal pollution in China had reached 16.1%, and in the construction of ecological civilization in the 13th Five-Year Plan, China has made the prevention and control of soil heavy metal pollution as the focus of prevention and control. Therefore, in this paper, four neural optimization network models, that is, radial basis neural network (RBFNN), generalized regression neural network (GRNN), wavelet neural network (WNN), and fuzzy neural network (FNN), are simulated and created to measure and correlate the soil heavy metal content in a city in northwest China and a city in central China from the actual situation in China. The simulations were conducted. Finally, by analyzing the comparison of predicted and true values of these four models on the test data of two sets of experimental data, the distribution of predicted differences to true values, and the calculation results of three error indicators, we found that WNN has the best prediction performance when using RBFNN, GRNN, WNN, and FNN for soil heavy metal content prediction.

Apart from the existing technology on image compression represented by series of JPEG, MPEG and H.26x standards, new technology such as Wavelet Neural Networks (WNN) and genetic algorithms are being developed to explore the future of image coding. Successful applications of WNN in the case of function approximation become well established. This paper presents a direct solution method based on Multi Library WNN for color image compression and coding which consists to transform an RGB image into Luminance-Chrominance space and then segment the luminance in a set of m blocks n by n pixels. These blocks should be transferred row by row (1D input vector) to the input of our wavelet network. Every input vector will be considered as unknown functional mapping and then it will be approximated by the network.

The growing population and industrialization have resulted in an increased demand for energy, which has worsened environmental problems such as pollution and climate change. Renewable energy sources are considered a promising solution due to their environmental benefits and limited potential. This study examines the use of neural networks and time series analysis to predict electricity generation rates from renewable energy sources in Turkey. We use the LSTM, NNAR, and ELM models, all of which utilize the backpropagation algorithm for neural network forecasting. Additionally, we apply ARIMA, Holt’s trend, linear regression, mean, and exponential smoothing models for time series analysis. We evaluate the performance using the mean absolute error and root mean square error on the training and test data. The study showed that LSTM models outperformed the ARIMA (1,2,1), ARIMA (2,2,1), ARIMA (3,2,1), and NNAR methods in forecasting accuracy. Although the NNAR model initially had the lowest error, its linear predictions made it less suitable for practical applications. This study highlights the effectiveness of neural networks and time series analysis in predicting renewable energy sources. The ARIMA (1,2,1), LSTM and ARIMA (3,2,1) modeling methods are useful for optimizing the planning and management of Turkey's renewable energy future, contributing to a more sustainable energy landscape.

Short‐term photovoltaic (PV) energy generation forecasting models are important, stabilizing the power integration between the PV and the smart grid for artificial intelligence‐ (AI‐) driven internet of things (IoT) modeling of smart cities. With the recent development of AI and IoT technologies, it is possible for deep learning techniques to achieve more accurate energy generation forecasting results for the PV systems. Difficulties exist for the traditional PV energy generation forecasting method considering external feature variables, such as the seasonality. In this study, we propose a hybrid deep learning method that combines the clustering techniques, convolutional neural network (CNN), long short‐term memory (LSTM), and attention mechanism with the wireless sensor network to overcome the existing difficulties of the PV energy generation forecasting problem. The overall proposed method is divided into three stages, namely, clustering, training, and forecasting. In the clustering stage, correlation analysis and self‐organizing mapping are employed to select the highest relevant factors in historical data. In the training stage, a convolutional neural network, long short‐term memory neural network, and attention mechanism are combined to construct a hybrid deep learning model to perform the forecasting task. In the testing stage, the most appropriate training model is selected based on the month of the testing data. The experimental results showed significantly higher prediction accuracy rates for all time intervals compared to existing methods, including traditional artificial neural networks, long short‐term memory neural networks, and an algorithm combining long short‐term memory neural network and attention mechanism.

Detecting and classifying sharp transients in EEG (Electroencephalograph) recording by visual screening is a laborious and time-consuming task. That is why, there is an urgent need to construct a computer algorithm to detect automatically that type of EEG transient phenomena. The use of an artificial neural network as a classifier and wavelet analysis as pre-processing give promising results to answer that need. This paper proposes to develop a new method for the automatic detection of epileptic spikes based on Wavelet Neural Networks (WNN). A proper selection of scaling in WNN is introduced to overcome the problem of very long time duration during training. The result shows that proper selection of wavelet scaling can decrease training duration without decreasing WNN performance.

In order to cope with the efficient consumption and flexible regulation of resource scarcity due to grid integration of renewable energy sources, a scheduling strategy that takes into account the coordinated interaction of source, grid, load, and storage is proposed. In order to improve the accuracy of the dispatch, a BP neural network approach modified by a genetic algorithm is used to predict renewable energy sources and loads. The non-convex, non-linear optimal dispatch model of the distribution grid is transformed into a mixed integer programming model with optimal tides based on the second-order cone relaxation, variable substitution, and segmental linearization of the Big M method. In addition, the uncertainty of distributed renewable energy output and the flexibility of load demand re-response limit optimal dispatch on a single time scale, so the frequency of renewable energy and load forecasting is increased, and an optimal dispatch model with complementary time scales is developed. Finally, the IEEE 33-node distribution system was tested to verify the effectiveness of the proposed optimal dispatching strategy. The simulation results show an 18.28% improvement in the economy of the system and a 24.39% increase in the capacity to consume renewable energy.

Spatiotemporal prediction of offshore wind fields based on a hybrid deep learning model for maritime navigation

Modern power systems are affected by many sources of uncertainty, driven by the spread of renewable generation, by the development of liberalized energy market systems and by the intrinsic random behavior of the final energy customers. Forecasting is, therefore, a crucial task in planning and managing modern power systems at any level: from transmission to distribution networks, and in also the new context of smart grids. Recent trends suggest the suitability of ensemble approaches in order to increase the versatility and robustness of forecasting systems. Stacking, boosting, and bagging techniques have recently started to attract the interest of power system practitioners. This book addresses the development of new, advanced, ensemble forecasting methods applied to power systems, collecting recent contributions to the development of accurate forecasts of energy-related variables by some of the most qualified experts in energy forecasting. Typical areas of research (renewable energy forecasting, load forecasting, energy price forecasting) are investigated, with relevant applications to the use of forecasts in energy management systems.