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Typical Building Thermal and Thermal Load Forecasting Based on Wavelet Neural Network
2020
Procedia Computer Science
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Based on the historical data of the cold/heat/electrical load of five typical buildings, the wavelet neural network is used for cold/heat/electrical short-term load forecasting. . ...
Based on the historical data of the cold/heat/electrical load of five typical buildings, the wavelet neural network is used for cold/heat/electrical short-term load forecasting. . ...
Based on the historical data of cold/heat/electrical load, this paper uses the wavelet prediction method of wavelet neural network to predict the cold/heat/electrical load of the same section, and provides ...
doi:10.1016/j.procs.2020.02.051
fatcat:wjjn5t6g5vbwnlgxj32oergixe
Wavelet-based multi-resolution analysis and artificial neural networks for forecasting temperature and thermal power consumption
2011
Engineering applications of artificial intelligence
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The proposed short-term forecast method is based on the concept of time series and uses both a wavelet-based multi-resolution analysis and multi-layer artificial neural networks. ...
topology of the neural networks used. ...
In 2000, Sharif and Taylor [13] used separate artificial neural networks for load forecasting of one hour to four hours ahead. ...
doi:10.1016/j.engappai.2010.09.003
fatcat:vjbnsnq5bzgnnp5alvurlisamm
Short-term Load Forecasting for Microgrids Based on Discrete Wavelet Transform and BP Neural Network
2014
Open Electrical & Electronic Engineering Journal
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At length, BP natural network is employed you forecast the micro grid load. ...
The final result proves that the forecasting precision of the method we propose is obviously better than the traditional ones. ...
Science Research Base of Hebei Province". ...
doi:10.2174/1874129001408010738
fatcat:l2yyk5vgvvblbhodpsesbfb2hq
A Condition Monitoring System Based On Dyadic Wavelet Transform Using Thermal Image
2019
VOLUME-8 ISSUE-10, AUGUST 2019, REGULAR ISSUE
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A dyadic wavelet transform gives an excellent output because of the different levels of the wavelet coefficient of the image. ...
The proposed method presents an optimal value such as mean, standard deviation and entropy for the decomposition and reconstruction of the thermal image. ...
Neural network can forecast threshold related to operating condition, which is utilized to forecast the motor condition. ...
doi:10.35940/ijitee.j8900.0881019
fatcat:jxg7qkr43fhmpb3b7l4n7ip3wi
State of the art of machine learning models in energy systems, a systematic review
2020
Zenodo
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Machine learning (ML) models have been widely used in diverse applications of energy systems such as design, modeling, complex mappings, system identification, performance prediction, and load forecasting ...
Furthermore, a comprehensive review of the literature represents an assessment and performance evaluation of the ML models, their applications and a discussion on the major challenges and opportunities ...
Wavelet neural networks Wavelet neural networks (WNN) benefits both theory of wavelets and neural networks and combines them. This method contains a FFNN with one hidden layer. ...
doi:10.5281/zenodo.4056884
fatcat:yf6gjoevffc2xnzoyjlc3pxbii
Thermal Design Space Exploration of 3D Die Stacked Multi-core Processors Using Geospatial-Based Predictive Models
[chapter]
2009
Lecture Notes in Computer Science
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Unlike other analytical techniques, our predictive models can forecast the location, size and temperature of thermal hotspots. ...
We evaluate the efficiency of using the models for predicting within-die and cross-dies thermal spatial characteristics of 3D multi-core architectures with widely varied design choices (e.g. microarchitecture ...
Instead of inferring the spatial thermal behavior via exhaustively obtaining temperature on each individual location, we employ wavelet analysis to approximate it and then use a neural network to forecast ...
doi:10.1007/978-3-540-93799-9_7
fatcat:laga2dogdvc5pdn23nc3unekp4
Fuzzy Wavelet Neural Networks for City Electric Energy Consumption Forecasting
2012
Energy Procedia
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The prediction effect of wavelet neural network prediction model is proved in matlab7.0 simulation environment. ...
neural network. ...
of fuzzy wavelet neural network Wavelet network is a new type feed forward network based on wavelet analysis. ...
doi:10.1016/j.egypro.2012.02.248
fatcat:7qv7x5iqw5eqlnu3nffwwoqb7y
State of the Art of Machine Learning Models in Energy Systems, a Systematic Review
2019
Energies
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This paper presents the state of the art of ML models used in energy systems along with a novel taxonomy of models and applications. ...
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. ...
The presented method was based on artificial neural networks and wavelet decomposition. ...
doi:10.3390/en12071301
fatcat:vzwylqto3zdjhofwfot7jdpvce
LİNYİT YAKITLI TERMİK SANTRALDE ELEKTRİK ENERJİSİ ÜRETİMİNİN ÇOK ADIMLI İLERİ TAHMİNİ
2021
Mühendislik Bilimleri ve Tasarım Dergisi
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Keywords Abstract ARIMA, NAR, Artificial Neural Network, Time Series, Multi-Step Forward Forecast. ...
This paper presents multi-step forward forecasting studies using real-time generated electrical power time series. ...
Conflict of Interest No conflict of interest was declared by the authors. ...
doi:10.21923/jesd.837788
fatcat:ulj4zepqfvd2lj5xi2jviq4ew4
Synthesis of the AC and DC Drives Fault Diagnosis Method for the Cyber-physical Systems of Building Robots
2018
MATEC Web of Conferences
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Based on the received information has been developed a neural classification network which makes it possible to reveal the current state of the object. ...
As a result of numerous experiments has been revealed the dependence of measurement of wavelet transformation coefficients on the characteristic scales of a serviceable and faulty engine under different ...
The goal of the neural network training is to distribute the five input signals to four classes: «11» -functional unload, «12» -functional loaded, «21» -faulty unload, «22» -faulty loaded. ...
doi:10.1051/matecconf/201825103060
fatcat:dwomm24wg5c6fh2s5txgvbpkie
Hybrid Model Combined Fuzzy Multi-Objective Decision Making with Feed Forward Neural Network (F-MODM-FFNN) For Very Short-Term Load Forecasting Based on Weather Data
2020
International Journal of Intelligent Engineering and Systems
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This research paper proposes a new hybrid methodology for very short-term load forecasting of hourly or designed to predict load for 1 hour ahead. ...
The proposed hybrid methodology is based on weather data especially for optimizing the operation of power generating electricity from thermal generation. ...
Acknowledgments Authors gratefully and wish to thank acknowledge the support Department of Electrical Engineering and Intelligence Power System Lab. for their valuable suggestions as well as Universitas ...
doi:10.22266/ijies2020.0831.16
fatcat:xfd2icux4zgkxcbh7gy5uvmhxm
Smart Dispatch and Demand Forecasting for Large Grid Operations with Integrated Renewable Resources
[chapter]
2011
Renewable Energy - Trends and Applications
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Within the wavelet neural network framework, the covariance matrices of Kalman filters for individual frequency components contained forecasting quality information of individual load components. ...
Multiple-level Wavelet Neural Network In general, each (Neural Network) NN as shown in Figure 8 could be implemented as a feed-forward neural network being described by the following equation: 11 (, ...
The value of the "∆cost acceptable" will be very much dependent on the amount of risk one is willing to take for reliability purposes when dispatching the system. ...
doi:10.5772/25733
fatcat:mjlmjjqywnalnaglgaaf7plwfu
Chaotic Time Series Forecasting Approaches Using Machine Learning Techniques: A Review
2022
Symmetry
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This article is an open access article distributed under the terms and conditions of the Creative Commons Attribution (CC BY ...
Wavelet NN-Based Forecasting Approaches The merits of wavelet and neural networks are hybridized to form a new WNN to achieve better forecasting ability. ...
In some applications, it was proven that if the combination of a neural network and wavelet is used, the proposed model's efficiency is increased. ...
doi:10.3390/sym14050955
dblp:journals/symmetry/RamadeviB22
fatcat:3oa3go7rdzdurjl4yxcivjsbf4
The Optimal Dispatch of a Power System Containing Virtual Power Plants under Fog and Haze Weather
2016
Sustainability
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The wavelet neural network (WNN) model was employed to predict photovoltaic output and load, considering F-H weather, based on the idea of "similar days of F-H". ...
The effects of F-H weather on photovoltaic output forecast, load forecast and power system dispatch are discussed according to real case data. ...
a (determining the width of the wavelet and the frequency resolution). (4) Wavelet neural network: The WNN, based on the topology of the BP neural network, selects a certain wavelet basis function as ...
doi:10.3390/su8010071
fatcat:jdu5gwpq25eszg7iqafzsd3dh4
Forecasting Model of Silicon Content in Molten Iron Using Wavelet Decomposition and Artificial Neural Networks
2021
Metals
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Next, all subseries forecasts were determined through Nonlinear Autoregressive (NAR) networks, and finally, these forecasts were summed to furnish the long-term forecast of silicon content. ...
Due to the delay of the laboratory when delivering the silicon content measurement, the proposed algorithm considers a minimum useful forecasting horizon of 3 h ahead. ...
Conflicts of Interest: The authors declare no conflict of interest.The funders had no role in the design of the study; in the collection, analyses, or interpretation of data; in the writing of the manuscript ...
doi:10.3390/met11071001
fatcat:ppvneigmejh6dcwr2bniq4zf4e
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