Prediction in Photovoltaic Power by Neural Networks

The ability to forecast the power produced by renewable energy plants in the short and middle term is a key issue to allow a high-level penetration of the distributed generation into the grid infrastructure. Forecasting energy production is mandatory for dispatching and distribution issues, at the transmission system operator level, as well as the electrical distributor and power system operator levels. In this paper, we present three techniques based on neural and fuzzy neural networks, namely the radial basis function, the adaptive neuro-fuzzy inference system and the higher-order neuro-fuzzy inference system, which are well suited to predict data sequences stemming from real-world applications. The preliminary results concerning the prediction of the power generated by a large-scale photovoltaic plant in Italy confirm the reliability and accuracy of the proposed approaches

Forecasting the Profitability in the Firms Listed in Tehran Stock Exchange Using Data Envelopment Analysis and Artificial Neural Network

Profitability as the most important factor in decision-making, has always been considered by stake­holders in the company's profitability. Also can be a basis for evaluating the performance of the managers. The ability to predict the profitability can be very useful to help decision-makers. That's why one of the most important issues is the expected profitability. The importance of these forecasts depends on the amount of misalignment with reality. The amount of deviation is less than the forecast of higher accuracy. Although there are various methods for predicting but the use of artificial intelligence techniques is increasing due to fewer restriction. The aim of this study is to evaluate the predictive power of profitability using DEA and neutral network, to enhance the decision-making users of 2012 to 2015of 7 premier financial ratios were used as independent variables. Test results show that both of ANN and DEA have ability to forecast profitability and given that neutral network prediction accuracy is higher than the DEA, the model predict better the profitability of companies

IMPLEMENTATION OF ARTIFICIAL NEURAL NETWORKS BASED AI CONCEPTS TO THE SMART GRID

 ICT and energy are two economic domains that became among the most influential to the growth of modern society. These, in the same time, due to exploitation of natural resources and producing unwanted effects to the environment, represent a kind of menace to the eco system and the human future. Implementation of measures to mitigate these unwanted effects established a new paradigm of production and distribution of electrical energy named smart grid. It relies on many novelties that improve the production, distribution and consumption of electricity among which one of the most important is the ICT. Among the ICT concepts implemented in modern smart grid one recognizes the artificial intelligence and, specifically the artificial neural network. Here, after reviewing the subject and setting the case, we are reporting some of our newest results aiming at broadening the set of tools being offered by ICT to the smart grid. We will describe our result in prediction of electricity demand and characterization of new threats to the security of the ICT that may use the grid as a carrier of the attack. We will use artificial neural networks (ANNs) as a tool in both subjects

Прогнозування попиту на пасажирські перевезення таксі методами нейронної мережі

Peculiarities of forecasting the demand for passenger transportation by taxi by neural network methods for different sets of input data, composition of network architecture parameters, hardware configuration and its capacity are considered. It was found that to reduce the waiting time for new orders and the distance to customers, it is advisable to use appropriate information and analytical systems, the work of which is based on artificial intelligence. This will solve the problem of demand for taxi transportation in the relevant period of the day, taking into account weather conditions, holidays, weekends and working days, as well as the seasons. Taking into account the existing transport facilities - flights, trains or buses significantly improves the work of such an advisory system. The hybrid architecture of the neural-phase network used in the work allows to simultaneously solve the problem of short-term forecasting of demand for passenger taxis, as well as to diagnose the network itself, which is to detect abrupt changes in the properties of the computing process. To achieve the appropriate accuracy of the forecast, the work developed input sets in the amount of 4.5 million taxi trips. To reduce the duration of the neural network training procedure, parallel calculations are organized between different network nodes using graphics processors. Neural network training on the CPU, one and two GPUs, respectively. It was found that the organization of parallel computing on several GPUs does not always reduce the duration of the network learning procedure, as the cost of synchronizing gradients between active processes far outweighs the benefits of parallel computing. It is established that with a large amount of data for the organization of parallel calculations and the corresponding architecture of the neural network, it is possible to achieve some reduction in the duration of the training procedure. It is determined that the reduction of the duration of the neural network learning procedure depends on the following factors: its architecture, the number of learning parameters, hardware configuration and organization of parallel calculations.Розглянуто особливості прогнозування попиту на пасажирські перевезення таксі методами нейронної мережі за різних наборів вхідних даних, складу параметрів архітектури мережі, конфігурації апаратного забезпечення та його потужності. З'ясовано, що для зменшення тривалості очікування нових замовлень та відстані до клієнтів доцільно використовувати відповідні інформаційно-аналітичні системи, робота яких ґрунтується на штучному інтелекті. Це дасть змогу вирішити проблему попиту на перевезення таксі у відповідний період доби з врахуванням погодних умов, святкових, вихідних і робочих днів, а також пори року. Врахування ж наявних транспортних об'єктів – авіарейсів, потягів чи автобусів значно покращують роботу такої дорадчої системи. Використана в роботі гібридна архітектура нейро-фаззі мережі дає змогу одночасно вирішувати завдання короткотермінового прогнозування попиту на пасажирські перевезення таксі, а також проводити діагностику самої мережі, що полягає у виявленні різких змін властивостей обчислювального процесу. Для досягнення відповідної точності прогнозу в роботі опрацьовано набори вхідних даних у кількості 4,5 млн поїздок таксі. Для зменшення тривалості процедури навчання нейронної мережі організовано паралельні обчислення між різними вузлами мережі за допомогою графічних процесорів. Проведено навчання нейронної мережі на центральному процесорі, одному та двох графічних процесорах відповідно. З'ясовано, що організація паралельних обчислень на декількох графічних процесорах не завжди зменшує тривалість процедури навчання мережі, оскільки витрати на синхронізацію градієнтів між активними процесами значно перевищують користь від паралельних розрахунків. Встановлено, що за умови великого обсягу даних для організації паралельних обчислень та відповідної архітектури нейронної мережі можна досягти деякого зменшення тривалості процедури її навчання. Визначено, що зменшення тривалості процедури навчання нейронної мережі залежить від таких чинників: її архітектури, кількості параметрів навчання, конфігурації апаратного забезпечення та організації паралельних розрахунків

Futuristic Air Compressor System Design and Operation by Using Artificial Intelligence

Indiana University-Purdue University Indianapolis (IUPUI)The compressed air system is widely used throughout the industry. Air compressors are one of the most costly systems to operate in industrial plants in terms of energy consumption. Therefore, it becomes one of the primary targets when it comes to electrical energy and load management practices. Load forecasting is the first step in developing energy management systems both on the supply and user side. A comprehensive literature review has been conducted, and there was a need to study if predicting compressed air system’s load is a possibility. System’s load profile will be valuable to the industry practitioners as well as related software providers in developing better practice and tools for load management and look-ahead scheduling programs. Feed forward neural networks (FFNN) and long short-term memory (LSTM) techniques have been used to perform 15 minutes ahead prediction. Three cases of different sizes and control methods have been studied. The results proved the possibility of the forecast. In this study two control methods have been developed by using the prediction. The first control method is designed for variable speed driven air compressors. The goal was to decrease the maximum electrical load for the air compressor by using the system's full operational capabilities and the air receiver tank. This goal has been achieved by optimizing the system operation and developing a practical control method. The results can be used to decrease the maximum electrical load consumed by the system as well as assuring the sufficient air for the users during the peak compressed air demand by users. This method can also prevent backup or secondary systems from running during the peak compressed air demand which can result in more energy and demand savings. Load management plays a pivotal role and developing maximum load reduction methods by users can result in more sustainability as well as the cost reduction for developing sustainable energy production sources. The last part of this research is concentrated on reducing the energy consumed by load/unload controlled air compressors. Two novel control methods have been introduced. One method uses the prediction as input, and the other one doesn't require prediction. Both of them resulted in energy consumption reduction by increasing the off period with the same compressed air output or in other words without sacrificing the required compressed air needed for production.2019-12-0

Short term load forecasting based on hybrid artificial neural networks and particle swarm optimisation

Short term load forecasting (STLF) is the prediction of electrical load for a period that ranges from the next minute to a week. The main objectives of the STLF function are to predict future load for the generation scheduling at power stations; assessment of the security of the power system as well as for timely dispatching of electrical power. STLF is primarily required to determine the most economic manner in which an electrical utility can schedule generation resources without compromising on the reliability requirements, operational constraints, policies and physical environmental and equipment limitations. Another application of the STLF is for predictive assessment of the power system security. This system load forecast is an essential data requirement for off-line network analysis in order to determine conditions under which a system may become vulnerable. This information allows the dispatcher to prepare the necessary corrective actions. The third application of STLF is to provide the system dispatcher with more recent information i.e., the most recent forecast with the latest weather prediction and random behaviour taken into account. The dispatcher needs this information to operate the system economically and reliably. Due to the sensitivities surrounding a load forecast, it thus becomes crucial that the forecasting error is minimised. There are various methods that are used for short term load forecasting, namely; statistical methods and computational intelligence methods. Statistical methods are known as the regression methods which forecast the future electrical load based on historic time series load information. These methods have been in use for many years however due to the dynamic changes in the power system today such as the introduction of Independent Power Producers (IPPs) onto the grid; it becomes difficult to use these methods because they are very static and inflexible i.e. they cannot be manipulated by including rules or expert knowledge in order to counter the effect of any sudden changes in the power system. Their inability to adapt to the changing behaviour of the power system thus leads to high forecasting errors. Computational intelligence (CI) methods however are dynamic and are able to learn by experience. Short term load forecasts have been conducted by using various CI methods such as Artificial Neural Networks (ANNs), Genetic Algorithms (GAs), Fuzzy Logic (FL), Expert Systems (ES), and Particle Swarm Optimisation (PSO). Hybrid versions of these methods, where two or more CI methods are amalgamated in a process to forecast future load, have also been used. iv In this research, a traditional forecasting technique, Multiple Linear Regression (MLR), was compared with a CI technique, Artificial Neural Networks. ANN was also compared with another neural network method namely Elman Recurrent Neural Network (ERNN) to determine whether a more neural network method with memory yields better results as compared to ANN