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The alternation between cloudy and clear skies alters the photovoltaic production. This makes it necessary to anticipate these disturbances hours in advance for the correct operation of the electricity distribution plants and networks. In this paper, two short-term forecasting models (3 h) are developed to forecast the photovoltaic production in an integrated plant in the CIESOL building of the University of Almería. The methodology used is based on sky camera images and Artificial Intelligence techniques. Two models have been developed and compared applying artificial neural network (ANN) and support vector machine (SVM) techniques. The global irradiance predicted using sky camera images is used as an input variable in both models. In addition, the operational status of the plants has been included as an input parameter through the performance ratio. The results have shown that the errors made by ANN and SVM are very similar. For all sky conditions, the uncertainty of the production forecast differs by less than 2% from the uncertainty of the solar resource, which is the main source of error in the production models developed. [ABSTRACT FROM AUTHOR]

• A new hybrid prediction system is proposed to predict wind speed. • Simplex and chaotic mapping optimization cuckoo algorithm are proposed. • An uncertainty prediction model based on Monte Carlo method is proposed. • The energy theory is proposed to optimize the decomposition mode number. • Theories proofs and experiments verify the effectiveness of the model. Wind energy has strong volatility and intermittent. Accurate wind speed prediction can not only improve the safety of the system, but also optimize dispatch and reduce economic losses. However, previous studies tend to ignore the influence of virtual components and lack effective identification of wind speed characteristics and a robust interval prediction scheme, resulting in poor results. To bridge these gaps, this paper proposes an energy theory method to solve the problem of modal over-decomposition. The study also combines effective modal recognition, uses different prediction methods according to modal characteristics and proposes a set of new optimization algorithms to improve nonlinear prediction capabilities. Finally, based on Monte Carlo theory, a set of interval prediction schemes that can adapt to different error characteristics are proposed. Under the verification of wind speed data in Changma, China and Sotavento, Spain. The mean absolute percentage error of wind speed deterministic prediction reaches 4.22% and 5.82%, respectively. The coverage rate of wind speed uncertainty prediction meets different confidence requirements, and the average interval width is still less than 2.5 m/s at 90% confidence. The results show that the forecasting system proposed in this paper is significantly better than all the comparative forecasting schemes, which can reduce the risk of fluctuations and improve the stability and safety of the wind power system. [ABSTRACT FROM AUTHOR]

The paper analyses and compares the merit order effects of photovoltaic and wind power on final electricity prices in the Spanish spot market and the cost of electricity tariffs. Artificial intelligence techniques are used to model the electricity auction clearing process. Several algorithms are studied before the M5P learning algorithm is finally applied to create a tree model of the spot market. Electricity tariffs for domestic consumers are also recalculated for fictional scenarios with no photovoltaic or wind power production. The conclusion is that the influence of photovoltaic and wind power is uneven. Wind power reduces final electricity prices by €9.10/MW h, generating an overall saving for the system of €364.0 million and for the average domestic consumer of €1.95 using 2012 figures; photovoltaic power reduces electricity prices by €2.18/MW h, generating an overall cost overrun for the system of €2034.1 million and for the average domestic consumer of €38.82. [ABSTRACT FROM AUTHOR]

Abstract: Land use and land covers (LULC) maps are remote sensing products that are used to classify areas into different landscapes. Data fusion for remote sensing is becoming an important tool to improve classical approaches. In addition, artificial intelligence techniques such as machine learning or evolutive computation are often applied to improve the final LULC classification. In this paper, a hybrid artificial intelligence method based on an ensemble of multiple classifiers to improve LULC map accuracy is shown. The method works in two processing levels: first, an evolutionary algorithm (EA) for label-dependent feature weighting transforms the feature space by assigning different weights to every attribute depending on the class. Then a statistical raster from LIDAR and image data fusion is built following a pixel-oriented and feature-based strategy that uses a support vector machine (SVM) and a weighted k-NN restricted stacking. A classical SVM, the original restricted stacking (R-STACK) and the current improved method (EVOR-STACK) are compared. The results show that the evolutive approach obtains the best results in the context of the real data from a riparian area in southern Spain. [Copyright &y& Elsevier]

On-Line monitoring evaluates instrument channel performance by assessing its consistency with other plant indications. Industry and EPRI experience at several plants has shown this overall approach to be very effective in identifying instrument channels that are exhibiting degrading or inconsistent performance characteristics “On-Line Monitoring of Instrument Channel Performance by EPRI (2000)”.On-Line monitoring of instrument channels provides information about the condition of the monitored channels through accurate, more frequent monitoring of each channel''s performance over time. This type of performance monitoring is a methodology that offers an alternate approach to traditional time-directed calibration. On-line monitoring of these channels can provide an assessment of instrument performance and provide a basis for determining when adjustments are necessary. Elimination or reduction of unnecessary field calibrations can reduce associated labor costs, reduce personnel radiation exposure and reduce the potential for miss-calibration.PEANO “A Neuro-Fuzzy Model Applied to Full Range Signal Validation of PWR Nuclear Power Plant Data by Fantoni (2000)” is a system for on-line calibration monitoring developed in the years 1995–2000 at the Institutt for energiteknikk (IFE), Norway, which makes use of Artificial Intelligence techniques for its purpose. The system has been tested successfully in Europe in off-line tests with EDF (France), Tecnatom (Spain) and ENEA (Italy). PEANO is currently installed and used for on-line monitoring at the HBWR reactor in Halden. This paper describes the results of performance tests on PEANO with real data from a US PWR plant, in the framework of a co-operation among IFE, EPRI and Edan Engineering, to evaluate the potentials of PEANO for future installations in US nuclear plants. [Copyright &y& Elsevier]