scholarly journals A Hybrid Optimization Approach for Autonomy Enhancement of Nearly-Zero-Energy Buildings Based on Battery Performance and Artificial Neural Networks

Energies ◽  
2020 ◽  
Vol 13 (14) ◽  
pp. 3680
Author(s):  
Giorgos S. Georgiou ◽  
Pavlos Nikolaidis ◽  
Soteris A. Kalogirou ◽  
Paul Christodoulides
Keyword(s):  
Neural Networks ◽  
Artificial Neural Networks ◽  
Renewable Energy ◽  
Real Time ◽  
Optimization Technique ◽  
Zero Energy ◽  
Eu Directive ◽  
Import And Export ◽  
Artificial Neural ◽  
Zero Energy Buildings

Reducing the primary energy consumption in buildings and simultaneously increasing self-consumption from renewable energy sources in nearly-zero-energy buildings, as per the 2010/31/EU directive, is crucial nowadays. This work solved the problem of nearly zeroing the net grid electrical energy in buildings in real time. This target was achieved using linear programming (LP)—a convex optimization technique leading to global solutions—to optimally decide the daily charging or discharging (dispatch) of the energy storage in an adaptive manner, in real time, and hence control and minimize both the import and export grid energies. LP was assisted by equally powerful methods, such as artificial neural networks (ANN) for forecasting the building’s load demand and photovoltaic (PV) on a 24 hour basis, and genetic algorithm (GA)—a heuristic optimization technique—for driving the optimum dispatch. Moreover, to address the non-linear nature of the battery and model the energy dispatch in a more realistic manner, the proven freeware system advisor model (SAM) of National Renewable Energy Laboratory (NREL) was integrated with the proposed approach to give the final dispatch. Assessing the case of a building, the results showed that the annual hourly profile of the import and export energies was smoothed and flattened, as compared to the cases without storage and/or using a conventional controller. With the proposed approach, the annual aggregated grid usage was reduced by 53% and the building’s annual energy needs were covered by the renewable energy system at a rate of 60%. It was therefore concluded that the proposed hybrid methodology can provide a tool to maximize the autonomy of nearly-zero-energy buildings and bring them a step closer to implementation.

MODELING OF NEURAL PREDICTIVE CONTROL OF IRRIGATION MACHINES

2021 ◽  
pp. 14-22
Author(s):  
G. N. KAMYSHOVA ◽  
Keyword(s):  
Neural Networks ◽  
Artificial Neural Networks ◽  
Real Time ◽  
Predictive Control ◽  
Intelligent Control ◽  
Machine Learning Algorithms ◽  
Intelligent Control Systems ◽  
Artificial Neural ◽  
The One ◽  
Neural Predictive Control

The purpose of the study is to develop new scientific approaches to improve the efficiency of irrigation machines. Modern digital technologies allow the collection of data, their analysis and operational management of equipment and technological processes, often in real time. All this allows, on the one hand, applying new approaches to modeling technical systems and processes (the so-called “data-driven models”), on the other hand, it requires the development of fundamentally new models, which will be based on the methods of artificial intelligence (artificial neural networks, fuzzy logic, machine learning algorithms and etc.).The analysis of the tracks and the actual speeds of the irrigation machines in real time showed their significant deviations in the range from the specified speed, which leads to a deterioration in the irrigation parameters. We have developed an irrigation machine’s control model based on predictive control approaches and the theory of artificial neural networks. Application of the model makes it possible to implement control algorithms with predicting the response of the irrigation machine to the control signal. A diagram of an algorithm for constructing predictive control, a structure of a neuroregulator and tools for its synthesis using modern software are proposed. The versatility of the model makes it possible to use it both to improve the efficiency of management of existing irrigation machines and to develop new ones with integrated intelligent control systems.

Real-Time Pricing and Hedging of Options on Currency Futures with Artificial Neural Networks

2014 ◽  
Vol 33 (6) ◽  
pp. 419-432 ◽  
Author(s):  
Christian von Spreckelsen ◽  
Hans-Jörg von Mettenheim ◽  
Michael H. Breitner
Keyword(s):  
Neural Networks ◽  
Artificial Neural Networks ◽  
Real Time ◽  
Currency Futures ◽  
Artificial Neural ◽  
Real Time Pricing

The Forecasting of Renewable Energy Generation for Turkey by Artificial Neural Networks and a Auto-Regressive Integrated Moving-Average Model -2023 Generation Targets by Renewable Energy Resources

2021 ◽  
Author(s):  
özlem karadag albayrak
Keyword(s):  
Neural Networks ◽  
Artificial Neural Networks ◽  
Renewable Energy ◽  
Moving Average ◽  
Renewable Energy Sources ◽  
Energy Generation ◽  
Energy Sources ◽  
Ann Model ◽  
Artificial Neural ◽  
Renewable Energy Generation

Abstract Turkey attaches particular importance to energy generation by renewable energy sources in order to remove negative economic, environmental and social effects caused by fossil resources in energy generation. Renewable energy sources are domestic and do not have any negative effect, such as external dependence in energy and greenhouse gas, caused by fossil resources and which constitute a threat for sustainable economic development. In this respect, the prediction of energy amount to be generated by Renewable Energy (RES) is highly important for Turkey. In this study, a generation forecasting was carried out by Artificial Neural Networks (ANN) and Autoregressive Integrated Moving Average (ARIMA) methods by utilising the renewable energy generation data between 1965-2019. While it was predicted by ANN that 127.516 TWh energy would be generated in 2023, this amount was estimated to be 45.457 TeraWatt Hour (TWh) by ARIMA (1.1.6) model. The Mean Absolute Percentage Error (MAPE) was calculated in order to specify the error margin of the forecasting models. This value was determined to be 13.1% by ANN model and 21.9% by ARIMA model. These results suggested that the ANN model provided a more accurate result. It is considered that the conclusions achieved in this study will be useful in energy planning and management.

Real-Time Recoloring Ishihara Plates Using Artificial Neural Networks for Helping Colorblind People

2020 ◽  
pp. 138-156
Author(s):  
Martín Montes Rivera ◽  
Alejandro Padilla ◽  
Juana Canul-Reich ◽  
Julio Ponce
Keyword(s):  
Artificial Intelligence ◽  
Neural Networks ◽  
Artificial Neural Networks ◽  
Real Time ◽  
Input Data ◽  
Traffic Signals ◽  
Educational Materials ◽  
Ishihara Test ◽  
Artificial Neural ◽  
Intelligence Technique

Vision sense is achieved using cells called rods (luminosity) and cones (color). Color perception is required when interacting with educational materials, industrial environments, traffic signals, among others, but colorblind people have difficulties perceiving colors. There are different tests for colorblindness like Ishihara plates test, which have numbers with colors that are confused with colorblindness. Advances in computer sciences produced digital assistants for colorblindness, but there are possibilities to improve them using artificial intelligence because its techniques have exhibited great results when classifying parameters. This chapter proposes the use of artificial neural networks, an artificial intelligence technique, for learning the colors that colorblind people cannot distinguish well by using as input data the Ishihara plates and recoloring the image by increasing its brightness. Results are tested with a real colorblind people who successfully pass the Ishihara test.

Artificial Neural Networks Estimation for Thicknesses of Multilayer Nano-Scale Films

2019 ◽  
Vol 962 ◽  
pp. 41-48
Author(s):  
Tzong Daw Wu ◽  
Jiun Shen Chen ◽  
Ching Pei Tseng ◽  
Cheng Chang Hsieh
Keyword(s):  
Thin Films ◽  
Neural Networks ◽  
Artificial Neural Networks ◽  
Real Time ◽  
Film Growth ◽  
Mean Squared Error ◽  
Squared Error ◽  
Thin Film Optics ◽  
The Mean ◽  
Artificial Neural

This study presents a real-time method for determining the thickness of each layer in multilayer thin films. Artificial neural networks (ANNs) were introduced to estimate thicknesses from a transmittance spectrum. After training via theoretical spectra which were generated by thin-film optics and modified by noise, ANNs were applied to estimate the thicknesses of four-layer nanoscale films which were TiO2, Ag, Ti, and TiO2 thin films assembled sequentially on polyethylene terephthalate (PET) substrates. The results reveal that the mean squared error of the estimation is 2.6 nm2, and is accurate enough to monitor film growth in real time.

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