1,873 research outputs found
Innovative Second-Generation Wavelets Construction With Recurrent Neural Networks for Solar Radiation Forecasting
Solar radiation prediction is an important challenge for the electrical
engineer because it is used to estimate the power developed by commercial
photovoltaic modules. This paper deals with the problem of solar radiation
prediction based on observed meteorological data. A 2-day forecast is obtained
by using novel wavelet recurrent neural networks (WRNNs). In fact, these WRNNS
are used to exploit the correlation between solar radiation and
timescale-related variations of wind speed, humidity, and temperature. The
input to the selected WRNN is provided by timescale-related bands of wavelet
coefficients obtained from meteorological time series. The experimental setup
available at the University of Catania, Italy, provided this information. The
novelty of this approach is that the proposed WRNN performs the prediction in
the wavelet domain and, in addition, also performs the inverse wavelet
transform, giving the predicted signal as output. The obtained simulation
results show a very low root-mean-square error compared to the results of the
solar radiation prediction approaches obtained by hybrid neural networks
reported in the recent literature
Wind Power Forecasting Methods Based on Deep Learning: A Survey
Accurate wind power forecasting in wind farm can effectively reduce the enormous impact on grid operation safety when high permeability intermittent power supply is connected to the power grid. Aiming to provide reference strategies for relevant researchers as well as practical applications, this paper attempts to provide the literature investigation and methods analysis of deep learning, enforcement learning and transfer learning in wind speed and wind power forecasting modeling. Usually, wind speed and wind power forecasting around a wind farm requires the calculation of the next moment of the definite state, which is usually achieved based on the state of the atmosphere that encompasses nearby atmospheric pressure, temperature, roughness, and obstacles. As an effective method of high-dimensional feature extraction, deep neural network can theoretically deal with arbitrary nonlinear transformation through proper structural design, such as adding noise to outputs, evolutionary learning used to optimize hidden layer weights, optimize the objective function so as to save information that can improve the output accuracy while filter out the irrelevant or less affected information for forecasting. The establishment of high-precision wind speed and wind power forecasting models is always a challenge due to the randomness, instantaneity and seasonal characteristics
Dynamic non-linear system modelling using wavelet-based soft computing techniques
The enormous number of complex systems results in the necessity of high-level and cost-efficient
modelling structures for the operators and system designers. Model-based approaches offer a very
challenging way to integrate a priori knowledge into the procedure. Soft computing based models
in particular, can successfully be applied in cases of highly nonlinear problems. A further reason
for dealing with so called soft computational model based techniques is that in real-world cases,
many times only partial, uncertain and/or inaccurate data is available.
Wavelet-Based soft computing techniques are considered, as one of the latest trends in system
identification/modelling. This thesis provides a comprehensive synopsis of the main wavelet-based
approaches to model the non-linear dynamical systems in real world problems in conjunction with
possible twists and novelties aiming for more accurate and less complex modelling structure.
Initially, an on-line structure and parameter design has been considered in an adaptive Neuro-
Fuzzy (NF) scheme. The problem of redundant membership functions and consequently fuzzy
rules is circumvented by applying an adaptive structure. The growth of a special type of Fungus
(Monascus ruber van Tieghem) is examined against several other approaches for further
justification of the proposed methodology.
By extending the line of research, two Morlet Wavelet Neural Network (WNN) structures have
been introduced. Increasing the accuracy and decreasing the computational cost are both the
primary targets of proposed novelties. Modifying the synoptic weights by replacing them with
Linear Combination Weights (LCW) and also imposing a Hybrid Learning Algorithm (HLA)
comprising of Gradient Descent (GD) and Recursive Least Square (RLS), are the tools utilised for
the above challenges. These two models differ from the point of view of structure while they share
the same HLA scheme. The second approach contains an additional Multiplication layer, plus its
hidden layer contains several sub-WNNs for each input dimension. The practical superiority of
these extensions is demonstrated by simulation and experimental results on real non-linear
dynamic system; Listeria Monocytogenes survival curves in Ultra-High Temperature (UHT)
whole milk, and consolidated with comprehensive comparison with other suggested schemes.
At the next stage, the extended clustering-based fuzzy version of the proposed WNN schemes, is
presented as the ultimate structure in this thesis. The proposed Fuzzy Wavelet Neural network
(FWNN) benefitted from Gaussian Mixture Models (GMMs) clustering feature, updated by a
modified Expectation-Maximization (EM) algorithm. One of the main aims of this thesis is to illustrate how the GMM-EM scheme could be used not only for detecting useful knowledge from
the data by building accurate regression, but also for the identification of complex systems.
The structure of FWNN is based on the basis of fuzzy rules including wavelet functions in the
consequent parts of rules. In order to improve the function approximation accuracy and general
capability of the FWNN system, an efficient hybrid learning approach is used to adjust the
parameters of dilation, translation, weights, and membership. Extended Kalman Filter (EKF) is
employed for wavelet parameters adjustment together with Weighted Least Square (WLS) which
is dedicated for the Linear Combination Weights fine-tuning. The results of a real-world
application of Short Time Load Forecasting (STLF) further re-enforced the plausibility of the
above technique
Data-driven Soft Sensors in the Process Industry
In the last two decades Soft Sensors established themselves as a valuable alternative to the traditional means for the acquisition of critical process variables, process monitoring and other tasks which are related to process control. This paper discusses characteristics of the process industry data which are critical for the development of data-driven Soft Sensors. These characteristics are common to a large number of process industry fields, like the chemical industry, bioprocess industry, steel industry, etc. The focus of this work is put on the data-driven Soft Sensors because of their growing popularity, already demonstrated usefulness and huge, though yet not completely realised, potential. A comprehensive selection of case studies covering the three most important Soft Sensor application fields, a general introduction to the most popular Soft Sensor modelling techniques as well as a discussion of some open issues in the Soft Sensor development and maintenance and their possible solutions are the main contributions of this work
Review of prognostic problem in condition-based maintenance.
International audienceprognostic is nowadays recognized as a key feature in maintenance strategies as it should allow avoiding inopportune maintenance spending. Real prognostic systems are however scarce in industry. That can be explained from different aspects, on of them being the difficulty of choosing an efficient technology ; many approaches to support the prognostic process exist, whose applicability is highly dependent on industrial constraints. Thus, the general purpose of the paper is to explore the way of performing failure prognostics so that manager can act consequently. Diffent aspects of prognostic are discussed. The prognostic process is (re)defined and an overview of prognostic metrics is given. Following that, the "prognostic approaches" are described. The whole aims at giving an overview of the prognostic area, both from the academic and industrial points of views
Development of Neurofuzzy Architectures for Electricity Price Forecasting
In 20th century, many countries have liberalized their electricity market. This power markets liberalization has directed generation companies as well as wholesale buyers to undertake a greater intense risk exposure compared to the old centralized framework. In this framework, electricity price prediction has become crucial for any market player in their decision‐making process as well as strategic planning. In this study, a prototype asymmetric‐based neuro‐fuzzy network (AGFINN) architecture has been implemented for short‐term electricity prices forecasting for ISO New England market. AGFINN framework has been designed through two different defuzzification schemes. Fuzzy clustering has been explored as an initial step for defining the fuzzy rules while an asymmetric Gaussian membership function has been utilized in the fuzzification part of the model. Results related to the minimum and maximum electricity prices for ISO New England, emphasize the superiority of the proposed model over well‐established learning‐based models
Artificial intelligence in wind speed forecasting: a review
Wind energy production has had accelerated growth in recent years, reaching an annual increase of 17% in 2021. Wind speed plays a crucial role in the stability required for power grid operation. However, wind intermittency makes accurate forecasting a complicated process. Implementing new technologies has allowed the development of hybrid models and techniques, improving wind speed forecasting accuracy. Additionally, statistical and artificial intelligence methods, especially artificial neural networks, have been applied to enhance the results. However, there is a concern about identifying the main factors influencing the forecasting process and providing a basis for estimation with artificial neural network models. This paper reviews and classifies the forecasting models used in recent years according to the input model type, the pre-processing and post-processing technique, the artificial neural network model, the prediction horizon, the steps ahead number, and the evaluation metric. The research results indicate that artificial neural network (ANN)-based models can provide accurate wind forecasting and essential information about the specific location of potential wind use for a power plant by understanding the future wind speed values
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