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The greatest concern facing renewable energy sources like wind is the uncertainty in production volumes as their generation ability is inherently dependent on weather conditions. When providing forecasts for newly commissioned wind farms there is a limited amount of historical power production data, while the number of potential features from different weather forecast providers is vast. Bayesian...
This article explores the problems of automated retail systems, which named are vending machines. The main problem is the formation of an assortment of a vending machine, the realization of which will bring maximum profit. As a modern analysis tool of consumer demand in retail trade artificial intelligence is regarded. Attention is focused on one of the methods of constructing artificial intelligence...
The significant role of predicting weather conditions in daily life, the new era of innovative machine learning approaches along with the availability of high volumes of data and high computer performance capabilities, creates increasing perspectives for novel improved short-range forecasting of main meteorological parameters. Among the various algorithms for forecasting parameters, ensemble learning...
The benefits of well-informed water management systems are related to the forecasting skills of hydrological variables. These benefits can be reflected in reducing economic and social losses to come. Therefore, the optimal design of water management projects frequently involves finding the methods or techniques that generate long sequences of hydrological data. These sequences considered as time series...
The occurrence of fire has a huge threat to people's life, therefore, in order to improve the quality of people's lives, we combines the support vector machine which is hot in recent years with the problem of home fire forecasting. Then a new method based on ensemble empirical mode decomposition and support vector machine is proposed. The results show that EEMD-SVM combination forecasting method has...
Wind speed forecasting has drawn a lot of research interests around the globe as it plays a key role in wind power plant operation. Accurate wind speed forecasting is vital for the integration of wind energy conversion system into existing electric power grids. The important factor of wind speed forecast is the choice of accurate prediction algorithm. Support Vector Machine Regression Model (SVM-R),...
Though in the era of big data, it remains a challenge to be tackled that the forecasting model with high accuracy and robustness needs to be built using small size samples. One effective tool of addressing this problem is the virtual sample generation (VSG), which can generate a mass of new virtual samples on the basis of small sample sets. The bootstrap method is adopted to feasibly resample the...
The aim of this work is to develop a robust model for short-term prediction of Photovoltaics (PV) generation. The model is structured with algorithms that belong to the technical field of computational intelligence. This approach provides the potential to form a forecasting system with high flexibility, efficiency and customization. The paper examines various combinations of inputs, in order to fully...
This work describes the calculation parameters of the drying agent, which includes the speed of air circulation, its temperature and humidity. The result of the calculation is the establishment of radial-basis artificial neural network, which allows to determine any parameter of drying agent at any point, represented by the coordinates X, Y, Z, and located within the drying chambers. Also in this...
Wind speed forecasting is critical to and challenging for wind energy industry. We present a combined AR-kNN regression model for short-term wind speed forecasting. Historical samples are selected to train the coefficients of a k-nearest-neighbor (kNN) regression model in order to capture the current variation pattern of wind speed. The training samples of the kNN model are combined with the recent...
In this paper, a new approach is presented for predicting landslide displacement using multi-gene genetic programming (MGGP). For the characteristic of MGGP which does not need specific assumptions, two real cases is used to prove the new approach is feasibility and validity.
Dust buildup on the surface of reflectors is a major challenge facing concentrated solar power (CSP) plants deployed in the MENA (Middle East and North Africa) region. Soiled CSP reflectors cause the efficiency of the solar field to drop. Thus, monitoring the loss of reflectance is essential to develop adequate cleaning strategies and evaluate the economics of the plants. The goal of this study is...
Unorganized neural networks — or unorganized machines - are recent developed architectures in the field of computational intelligence, in which the supervised tuning of the free parameters is restricted to the weights of the output layer, by means of a linear least square solution. The remaining weights are randomly generated and stand untrained which become the adjustment process simple and fast...
Forecasting electricity price allows market participants to make informed and sound decisions. Selecting the best training variables is often involved in forecasting in order to obtain optimal prediction. Support Vector Regression (SVR) provides an effective method to fit data and find minimal risk slack variables around a fit line. The best fit depends on the selected input feature set and the tuning...
To enable better smart charging solutions, this paper investigates the day-ahead probabilistic forecasting of the availability and the charging rate at charging stations for plug-in electric vehicles. Generalized linear models with logistic link functions are at the core of both forecast scenarios. Moreover, the availability forecast at a charging point is simply a binomial problem, whereas the charging...
The stock market is the most important institution for global investments all around the world. Among the possibles analysis, the study and forecasting of ultra-high-frequency time series is an interesting and great challenge to econometric modeling and statistical analysis due its complex behaviour. This work proposes a hybrid intelligent system to forecast ultra-high-frequency stock prices. The...
The Off-Grid systems are systems with an independence on the energy supply from external grid, whereas renewables (RES) are used as a sources of electric and heat energy. The main RES is photovoltaic power plant (PVP), however this source has the stochastic character of power supply. The stochastic character of PVP is given by dependency on a weather conditions. This brings a need of solar irradiance...
Today, Stock investment is an important part of the economy of the country. Therefore, forecasting changes in the behavior of market has become significantly important to shareholders. In the past years, classic methods often were used to forecast changes in the market behavior, but, in recent years, intelligent methods increasingly have been applied to forecast the behavior of stock market. Using...
Price forecasting has become essential tool in deregulated electricity market. It is used by utility operators for bidding in the competitive market to increase their profits and services. The models for electricity price forecasting can be mainly categorized into (i) Statistical models, (ii) Artificial Intelligence models & (iii) Hybrid models. AI based models, i.e., ANN have gained popularity...
The paper presents a low complexity recurrent Functional link Artificial Neural Network for predicting the time series data like the stock market indices over a time frame varying from one day ahead to one month ahead. Further an adaptive bioinspired Firefly algorithm is adopted here to find the optimal weights for the recurrent computationally efficient functional link neural network (RCEFLANN) using...
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