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51 |
A novel spatio-temporal wind power forecasting framework based on multi-output support vector machine and optimization strategy
چارچوب پیش بینی نیروی باد مکانی و مکانی رمان بر اساس ماشین بردار پشتیبانی چند خروجی و استراتژی بهینه سازی-2020 The integration of a large number of wind farms poses big challenges to the secure and
economical operation of power systems, and ultra-short-term wind power forecasting is an
effective solution. However, traditional approaches can only predict an individual wind farm
power at a time and ignore the spatio-temporal correlation of wind farms. In this paper, a novel
ultra-short-term forecasting framework based on spatio-temporal (ST) analysis, multi-output
support vector machine (MSVM) and grey wolf optimizer (GWO) which defined
ST-GWO-MSVM model is proposed to predict the output wind power from multiple wind farms;
the ST-GWO-MSVM model includes data analysis stage, parameters optimization stage, and
modeling stage. In the data analysis stage, the person correlation coefficient and partial
autocorrelation function are used to analyze the spatio-temporal correlation of wind power. In the
parameters optimization stage, to avoid obtaining the unreliable forecasting results due to the
parameters are chosen empirically, the GWO algorithm is used to optimize the kernel function
parameters of the MSVM model. In the modeling stage, an innovative forecasting model with
optimal parameter of MSVM is proposed to predict the output wind power of 15 wind farms.
Results show that the performance of ST-GWO-MSVM is better than other benchmark models in
terms of multiple-error metrics including fractional bias, direction accuracy, and improvement
percentages. Keywords: wind power forecasting | Spatio-temporal correlation | Multi-output support vector machine | Grey wolf optimizer | Combined forecasting approaches |
مقاله انگلیسی |
52 |
A novel photovoltaic-pumped hydro storage microgrid applicable to rural areas
رویکرد ذخیره سازی آب فتوولتائیک قابل اجرا در مناطق روستایی-2020 This paper proposes a novel photovoltaic-pumped hydro storage microgrid design, which is more cost-effective
than photovoltaic-battery systems. Existing irrigation infrastructure is modified in order to store energy at a low
cost. This energy storage system pumps water from the bottom of a water well to a reservoir at ground level to
store surplus energy in the form of gravitational potential energy. This stored water can be released back to the
well through a turbine to generate clean electricity when it is needed, or it can be used for irrigation. This
microgrid needs a complex management system that takes into account energy generation, energy demand,
water demand, energy tariff, and system losses to determine pump power, turbine flow rate, as well as irrigation
times. The proposed energy management system considers the current and future state of the system and
compares cost-saving and feed-in income for each decision by using two forecasting methods and a multi-level
optimisation algorithm. The performance of the management system is experimentally verified on a real pump
and turbine. The objective of this study is not only to manage pump power and turbine flow rate, but also to
manage irrigation times and water volume. The results show that adding irrigation and water management assist
the energy management system in using stored water more efficiently. As a result, electricity costs are reduced
by more than 31% compared to existing management methods. The proposed system is simulated in MATLAB to
calculate annual electricity costs. The payback period and lifetime benefit of the proposed storage are calculated
to investigate the economic aspects of the system. Keywords: Energy management system | Pumped hydro storage system | Energy storage system | Renewable energy | Solar photovoltaic system | Microgrid |
مقاله انگلیسی |
53 |
A photovoltaic power forecasting model based on dendritic neuron networks with the aid of wavelet transform
یک مدل پیش بینی قدرت فتوولتائیک مبتنی بر شبکه های عصبی دندریتیک با کمک تبدیل موجک-2020 The ever increasing proportion of photovoltaic (PV), which is, in effect, a random and intermittent energy source, makes PV power forecasting increasingly important for power grid stability. Artificial neural net- works (ANN) have become one of the commonly utilized methods in PV power prediction. Since there is no ideal theoretical guidance as yet on the determination of the number of hidden layers and hidden units, there are always abundant neurons in traditional neural networks in order to learn as many data characteristics as possible, which often results in overfitting and high computational costs. The dendritic model proposed in recent years has the characteristics of simple structure, fast convergence and better fitting ability. This paper proposes a PV power forecasting model based on the dendritic neuron networks, which seeks to improve the computational efficiency and prediction accuracy. In order to better extract characteristics of different frequencies of the input data, the approach introduces a wavelet transform. Firstly, the data is decomposed into high-frequency and low-frequency components via a wavelet trans- form. Thereafter, the input data of different frequencies obtained by the decomposition are transmitted respectively to different sub-models. Finally, the results of sub-models are reconstructed to obtain the final output. The proposed PV power forecasting model was tested upon actual photovoltaic datasets. Results obtained through simulation demonstrate significant improvement in terms of accuracy and efficiency. Keywords: Photovoltaic power | Very-short-term forecasting | Dendritic neural network | Wavelet transform |
مقاله انگلیسی |
54 |
Keep it simple stupid! A non-parametric kernel regression approach to forecast travel speeds
آن را احمقانه نگه دارید! یک روش رگرسیون هسته غیر پارامتری برای پیش بینی سرعت سفر-2020 The approach taken by the second place winner of the TRANSFOR prediction challenge is presented.
The challenge involves forecasting travel speeds on two arterial links in Xi’an City in
China for two five hour periods on a single day. Travel speeds are measured from trajectory
information on probe vehicles from a fleet of vehicles for a large sub-area of the city. After
experimenting with several deep learning methods, we settle on a simple non-parametric kernel
regression approach. The method, borrowed from previous work in fixed route transit predictions,
formalizes the intuition that in urban systems most failure patterns are recurrent. Our
choice is supported by test results where the method outperformed all evaluated neural architectures.
The results suggest simple methods are very competitive, particularly considering the
high lifecycle cost of deep learning models. Keywords: Traffic forecasting | Machine learning | Big data |
مقاله انگلیسی |
55 |
Forecasting client retention — A machine-learning approach
پیش بینی حفظ مشتری - یک رویکرد یادگیری ماشین-2020 In the age of big data, companies store practically all data on any client transaction. Making use of this data is
commonly done with machine-learning techniques so as to turn it into information that can be used to drive
business decisions. Our interest lies in using data on prepaid unitary services in a business-to-business setting to
forecast client retention: whether a particular client is at risk of being lost before they cease being clients. The
purpose of such a forecast is to provide the company with an opportunity to reach out to such clients as an effort
to ensure their retention.
We work with monthly records of client transactions: each client is represented as a series of purchases and
consumptions. We vary (1) the length of the time period used to make the forecast, (2) the length of a period of
inactivity after which a client is assumed to be lost, and (3) how far in advance the forecast is made. Our
experimental work finds that current machine-learning techniques able to adequately predict, well in advance,
which clients will be lost. This knowledge permits a company to focus marketing efforts on such clients as early
as three months in advance. Keywords: Client retention | Sales forecasting | Machine learning | Prepaid unitary services |
مقاله انگلیسی |
56 |
Nature-inspired metaheuristic ensemble model for forecasting energy consumption in residential buildings
مدل انعطاف پذیر فراشناختی با الهام از طبیعت برای پیش بینی مصرف انرژی در ساختمانهای مسکونی-2020 As the global economy expands, both residential and commercial buildings consume an increasing
proportion of the total energy that is used by buildings. Energy simulation and forecasting are important
in setting energy policy and making decisions in pursuit of sustainable development. This work develops
a new ensemble model, called the Evolutionary Neural Machine Inference Model (ENMIM), for estimating
energy consumption in residential buildings based on actual data. The ensemble model combines
two single supervised learning machines - least squares support vector regression (LSSVR), and the radial
basis function neural network (RBFNN) eand incorporates symbiotic organism search (SOS) to find
automatically its optimal tuning parameters. A set of real data, which were obtained from residential
buildings in Ho Chi Minh City, Viet Nam, as well as experimental data from the literature were used to
evaluate the performance of the developed model. Comparison results reveal that the ENMIM surpasses
other benchmark models with respect to predictive accuracy. This work proves that the developed
ensemble model is a promising alternative for the planning of energy management. Furthermore, the fact
that the ENMIM has greater predictive accuracy than other artificial intelligence techniques suggests that
the developed self-tuning ensemble model can be used in various disciplines. Keywords: Energy consumption | Residential buildings | Ensemble model | Artificial intelligence | Machine learning | Evolutionary optimization |
مقاله انگلیسی |
57 |
Optimizing hyperparameters of deep learning in predicting bus passengers based on simulated annealing
بهینه سازی پارامترهای یادگیری عمیق در پیش بینی مسافران اتوبوس مبتنی بر بازپخت شبیه سازی شده-2020 Bus is certainly one of the most widely used public transportation systems in a modern city because it
provides an inexpensive solution to public transportation users, such as commuters and tourists. Most
people would like to avoid taking a crowded bus on the way. That is why forecasting the number
of bus passengers has been a critical problem for years. The proposed method is inspired by the fact
that there is no easy way to know the suitable parameters for most of the deep learning methods
in solving the optimization problem of forecasting the number of passengers on a bus. To address
this issue, the proposed algorithm uses a simulated annealing (SA) to find out a suitable number of
neurons for each layer of a fully connected deep neural network (DNN) to enhance the accuracy rate in
solving this particular optimization problem. The proposed method is compared with support vector
machine, random forest, eXtreme gradient boosting, deep neural network, and deep neural network
with dropout for the data provided by the Taichung city smart transportation big data research center,
Taiwan (TSTBDRC). Our simulation results indicate that the proposed method outperforms all the other
forecasting methods for forecasting the number of bus passengers in terms of the accuracy rate and
the prediction time. Keywords: Bus transportation system | Simulated annealing | Deep learning | Hyperparameter optimization |
مقاله انگلیسی |
58 |
An AI Model for Oil Volatility Forecasting
یک مدل هوش مصنوعی برای پیش بینی نوسانات نفت-2020 Abstract—By introducing a genetic algorithm learning with a classifier system, we
construct an AI model for oil volatility forecasting on the basis of Internal Information and
External Information. The model provides decision support for mark-to-market portfolio
and risk management by forecasting whether 1-day-ahead volatility is above a given
threshold. Moreover, we explore the dynamic influencing mechanism of different types of
information through information usage frequency in the learning process. In particular, we
find that the jump component of oil realized volatility is efficient only in bull market, and
currency information contributes most rather than oil information in bear market.
Therefore, this article provides an AI method to forecast oil volatility as well as to improve
the information structure of forecasting models. |
مقاله انگلیسی |
59 |
Techniques Tanimoto correlated feature selection system and hybridization of clustering and boosting ensemble classification of remote sensed big data for weather forecasting
تکنیک های مربوط به سیستم انتخاب ویژگی Tanimoto و ترکیبی از خوشه بندی و افزایش طبقه بندی گروه از داده های بزرگ از راه دور برای پیش بینی آب و هوا-2020 Weather forecasting has been done using various techniques but still not efficient for handling the big remote
sensed data since the data comprises the more features. Hence the techniques degrade the forecasting accuracy
and take more prediction time. To enhance the prediction accuracy (PA) with minimal time, Tanimoto
Correlation based Combinatorial MAP Expected Clustering and Linear Program Boosting Classification (TCCMECLPBC)
Technique is proposed. At first, the data and features are gathered from big weather database.
After that, relevant features are selected through finding the similarity between the features. Tanimoto
Correlation Coefficient is used to find the similarity between the features for selecting the relevant features
with higher feature selection accuracy. After selecting the relevant features, MAP expected clustering process
is carried out to group the weather data for cluster formation. In this process, a number of cluster and
cluster centroids are initialized. In this clustering process, it includes two steps namely expectation (E)
and maximization (M) to discover maximum probability for grouping data into the cluster. After that, the
clustering result is given to Linear Program boosting classifier to improve the prediction performance. In this
classification, the weak classifier results are boosted to create strong classifier. The results evident that the
TC-CMECLPBC technique enhance the PA with lesser time and false positive rate (FPR) than the conventional
methods. Keywords: Big data | Tanimoto correlation | MAP expected | Boosting classification | Expectation | Maximization | Similarity | Clustering | Cluster centroids | Strong classifier | Weak classifier |
مقاله انگلیسی |
60 |
A series of forecasting models for seismic evaluation of dams based on ground motion meta-features
مجموعه ای از مدل های پیش بینی برای ارزیابی لرزه ای سدها بر اساس ویژگی های متا حرکت زمین-2020 Uncertainty quantification (UQ) due to seismic ground motions variability is an important task in risk-informed
condition assessment of infrastructures. Since performing multiple dynamic analyses is computationally expensive,
it is valuable to develop a series of forecasting models based on the unique ground motion characteristics.
This paper discusses the application of six different machine learning techniques on forecasting the structural
behavior of gravity dams. Various time-, frequency-, and intensity-dependent characteristics are extracted from
ground motion signals and used in machine learning. A large set of about 2000 real ground motions are used,
each includes about 35 meta-features. The major outcome of this study is to show the applicability of metamodeling-
based UQ in seismic safety evaluation of dams. As an intermediary result, the advantages of different
machine learning algorithms, as well as meta-feature selection possibility is discussed for the current dataset.
This paper proposes a feasibility study to reduce the computational costs in UQ of large-scale infra-structural
systems. Keywords: Uncertainty quantification | Dams | Forecasting | Machine learning | Big data |
مقاله انگلیسی |