Short-term forecasting of load and renewable energy using artifical neural network

Load forecasting is a technique used for the prediction of electrical load demands in battery management. In general, the aggregated level used for short-term electrical load forecasting (STLF) consists of either numerical or non-numerical information collected from multiple sources, which helps in obtaining accurate data and efficient forecasting. However, the aggregated level cannot precisely forecast the validation and testing phases of numerical data, including the real-time measurements of irradiance level (W/m2) and photovoltaic output power (W). Forecasting is also a challenge due to the fluctuations caused by the random usage of appliances in the existing weekly, diurnal, and annual cycle load data. In this study, we have overcome this challenge by using Artificial Neural Network (ANN) methods such as Bayesian Regularisation (BR) and Levenberg-Marquardt (LM) algorithms. The STLF achieved by ANN-based methods can improve the forecast accuracy. The overall performance of the BR and LM algorithms were analyzed during the development phases of the ANN. The input layer, hidden layer and output layer used to train and test the ANN together predict the 24-hour electricity demand. The results show that utilizing the LM and BR algorithms delivers a highly efficient architecture for renewable power estimation demand. © 2021 Seventh Sense Research Group

Mitigation of impulsive noise in OFDM channels using ANN technique

Abstract: Orthogonal frequency division multiplexer (OFDM) is a recent modulation scheme used to transmit signals across power line communication (PLC) channel due to its robustness against some known PLC problems. However, this scheme is greatly affected by the impulsive noise (IN) and often causes corruption with the transmitted bits. Different impulsive noise error correcting methods have been introduced and used to remove impulsive noise in OFDM systems. However, these techniques suffer some limitations and require much signal to noise ratio (SNR) power to operate. In this paper, an approach of designing an effective impulsive-noise error-correcting technique was introduced using three-known artificial neural network techniques (Levenberg-Marquardt, Scaled conjugate gradient, and Bayesian regularization). Findings suggest that both Bayesian regularization and Levenberg-Marquardt ANN techniques can be used to effectively remove the impulsive noise present in an OFDM channel and using the least SNR power

Effect of Environmental Conditions and Training Algorithms on the Efficiency of a NARX Based Approach to Predict PV Panel Power Output

Photovoltaic energy is volatile in nature since it depends on weather conditions. It is important to have an idea about the reliability and the economic feasibility of any new project to decide whether it is right to proceed with the installation of such a project. Hence, it is becoming fundamental to know renewable energy state and production that can be combined with other less variable and more predictable sources to justify the choice of regions for the new photovoltaic projects installation. The current research investigates the forecasting abilities of a NARX based approach. The influence of the meteorological data, such as irradiance, ambient temperature, and wind speed, and the impact of training algorithms on the performance of the NARX-based forecaster is studied. For this purpose, four models are discussed, each model is trained based on three training algorithms. The NARX model using a Bayesian Regularization algorithm, trained by the three meteorological data as inputs and the converted power output as output, outperforms the other models. It consists of a simple architecture with one input layer, a hidden layer containing 1O neurons, and an output layer, with a mean square error of 0.0085 W2 for the training phase and 0.0043 W2 testing phase, and the overall regression of 95.48%. This simplified architecture and low values of the mean square error and the regression coefficient suggest that they are promising photovoltaic output prediction tools, particularly in locations where few meteorological parameters are monitored

BLACK BOX EFFICIENCY MODELLING OF AN ELECTRIC DRIVE UNIT UTILIZING METHODS OF MACHINE LEARNING

The increasing electrification of powertrains leads to increased demands for the test technology to ensure the required functions. For conventional test rigs in particular, it is necessary to have knowledge of the test technology's capabilities that can be applied in practical testing. Modelling enables early knowledge of the test rigs dynamic capabilities and the feasibility of planned testing scenarios. This paper describes the modelling of complex subsystems by experimental modelling with artificial neural networks taking transmission efficiency as an example. For data generation, the experimental design and execution is described. The generated data is pre-processed with suitable methods and optimized for the neural networks. Modelling is executed with different variants of the inputs as well as different algorithms. The variants compare and compete with each other. The most suitable variant is validated using statistical methods and other adequate techniques. The result represents reality well and enables the performance investigation of the test systems in a realistic manner

A regressive machine-learning approach to the non-linear complex FAST model for hybrid floating offshore wind turbines with integrated oscillating water columns

Offshore wind energy is getting increasing attention as a clean alternative to the currently scarce fossil fuels mainly used in Europe's electricity supply. The further development and implementation of this kind of technology will help fighting global warming, allowing a more sustainable and decarbonized power generation. In this sense, the integration of Floating Offshore Wind Turbines (FOWTs) with Oscillating Water Columns (OWCs) devices arise as a promising solution for hybrid renewable energy production. In these systems, OWC modules are employed not only for wave energy generation but also for FOWTs stabilization and cost-efficiency. Nevertheless, analyzing and understanding the aero-hydro-servo-elastic floating structure control performance composes an intricate and challenging task. Even more, given the dynamical complexity increase that involves the incorporation of OWCs within the FOWT platform. In this regard, although some time and frequency domain models have been developed, they are complex, computationally inefficient and not suitable for neither real-time nor feedback control. In this context, this work presents a novel control-oriented regressive model for hybrid FOWT-OWCs platforms. The main objective is to take advantage of the predictive and forecasting capabilities of the deep-layered artificial neural networks (ANNs), jointly with their computational simplicity, to develop a feasible control-oriented and lightweight model compared to the aforementioned complex dynamical models. In order to achieve this objective, a deep-layered ANN model has been designed and trained to match the hybrid platform's structural performance. Then, the obtained scheme has been benchmarked against standard Multisurf-Wamit-FAST 5MW FOWT output data for different challenging scenarios in order to validate the model. The results demonstrate the adequate performance and accuracy of the proposed ANN control-oriented model, providing a great alternative for complex non-linear models traditionally used and allowing the implementation of advanced control schemes in a computationally convenient, straightforward, and easy way.This work was supported in part by the Basque Government through project IT1555-22 and through the projects PID2021-123543OB-C21 and PID2021-123543OB-C22 (MCIN/AEI/10.13039/501100011033/FEDER, UE). The authors would also like to thank the UPV/EHU for the financial support through the María Zambrano grant MAZAM22/15 and Margarita Salas grant MARSA22/09 (UPV-EHU/MIU/Next Generation, EU) and through grant PIF20/299 (UPV/EHU)

A Promising Wavelet Decomposition –NNARX Model to Predict Flood: Application to Kelantan River Flood

Flood is a major disaster that happens around the world. It has caused many casualties and massive destruction of property. Estimating the chance of a flood occurring depends on several factors, such as rainfall, the structure and the flow rate of the river. This research used the neural network autoregressive exogenous input (NNARX) model to predict floods. One of the research challenges was to develop accurate models and improve the forecasting model. This research aimed to improve the performance of the neural network model for flood prediction. A new technique was proposed for modelling nonlinear data of flood forecasting using the wavelet decomposition-NNARX approach. This paper discusses the process of identifying the parameters involved to make a forecast as the rainfall value requires the flow rate of the river and its water level. The original data were processed by wavelet decomposition and filtered to generate a new set of data for the NNARX prediction model where the process can be compared. This research compared the performance of the wavelet and the non-wavelet NNARX model. Experimental results showed that the proposed approach had better performance testing results in relation to its counterpart in terms of hourly forecast, with the mean square error (MSE) of 2.0491e-4 m2 compared to 6.1642e-4 m2, respectively. The proposed approach was also studied for long-term forecast up to 5 years, where the obtained MSE was higher, i.e., 0.0016 m2

Modeliranje sadržaja vlage i brzine sušenja određenih plodova solarnim sušenjem primjenom ANN-a

The aim of this work was to model the moisture content (MC) and drying rate (DR) using artificial neural network (ANN) methodology. Many architectures have been tested and the best topology was selected based on a trial and error method. The dataset was randomly divided into 60, 20, and 20 % for training, test, and validation stage of the ANN model, respectively. The best topology was 10-{29-13}-2 obtained with high correlation coefficient R (%) of {99.98, 98.41} and low root mean square error RMSE (%) (0.36, 6.29) for MC and DR, respectively. The obtained ANN can be used to interpolate the MC and DR with high accuracy. This work is licensed under a Creative Commons Attribution 4.0 International License.Cilj ovog rada bio je modelirati sadržaj vlage (MC) i brzinu sušenja (DR) primjenom metodologije umjetne neuronske mreže (ANN). Testirane su mnoge arhitekture, a najbolja topologija odabrana je na temelju metode pokušaja i pogrešaka. Skup podataka podijeljen je nasumično na 60, 20 i 20 % za fazu treninga, testa i validacije ANN modela. Najbolja topologija bila je 10-{29-13}-2 dobivena visokim koeficijentom korelacije R (%) od {99,98, 98,41} i niskom srednjom kvadratnom pogreškom RMSE (%) (0,36, 6,29) za MC, odnosno DR. Dobiveni ANN model može se s velikom točnošću primijeniti za interpolaciju MC-a i DR-a. Ovo djelo je dano na korištenje pod licencom Creative Commons Imenovanje 4.0 međunarodna

The design of an indirect method for the human presence monitoring in the intelligent building

This article describes the design and verification of the indirect method of predicting the course of CO2 concentration (ppm) from the measured temperature variables Tindoor (degrees C) and the relative humidity rH(indoor) (%) and the temperature T-outdoor (degrees C) using the Artificial Neural Network (ANN) with the Bayesian Regulation Method (BRM) for monitoring the presence of people in the individual premises in the Intelligent Administrative Building (IAB) using the PI System SW Tool (PI-Plant Information enterprise information system). The CA (Correlation Analysis), the MSE (Root Mean Squared Error) and the DTW (Dynamic Time Warping) criteria were used to verify and classify the results obtained. Within the proposed method, the LMS adaptive filter algorithm was used to remove the noise of the resulting predicted course. In order to verify the method, two long-term experiments were performed, specifically from February 1 to February 28, 2015, from June 1 to June 28, 2015 and from February 8 to February 14, 2015. For the best results of the trained ANN BRM within the prediction of CO2, the correlation coefficient R for the proposed method was up to 92%. The verification of the proposed method confirmed the possibility to use the presence of people of the monitored IAB premises for monitoring. The designed indirect method of CO2 prediction has potential for reducing the investment and operating costs of the IAB in relation to the reduction of the number of implemented sensors in the IAB within the process of management of operational and technical functions in the IAB. The article also describes the design and implementation of the FEIVISUAL visualization application for mobile devices, which monitors the technological processes in the IAB. This application is optimized for Android devices and is platform independent. The application requires implementation of an application server that communicates with the data server and the application developed. The data of the application developed is obtained from the data storage of the PI System via a PI Web REST API (Application Programming Integration) client.Web of Science8art. no. 2

Recurrent neural network based approach for estimating the dynamic evolution of grinding process variables

170 p.El proceso de rectificado es ampliamente utilizado para la fabricación de componentes de precisión por arranque de viruta por sus buenos acabados y excelentes tolerancias. Así, el modelado y el control del proceso de rectificado es altamente importante para alcanzar los requisitos económicos y de precisión de los clientes. Sin embargo, los modelos analíticos desarrollados hasta ahora están lejos de poder ser implementados en la industria. Es por ello que varias investigaciones han propuesto la utilización de técnicas inteligentes para el modelado del proceso de rectificado. Sin embargo, estas propuestas a) no generalizan para nuevas muelas y b) no tienen en cuenta el desgaste de la muela, efecto esencial para un buen modelo del proceso de rectificado. Es por ello que se propone la utilización de las redes neuronales recurrentes para estimar variables del proceso de rectificado que a) sean capaces de generalizar para muelas nuevas y b) que tenga en cuenta el desgaste de la muela, es decir, que sea capaz de estimar variables del proceso de rectificado mientras la muela se va desgastando. Así, tomando como base la metodología general, se han desarrollado sensores virtuales para la medida del desgaste de la muela y la rugosidad de la pieza, dos variables esenciales del proceso de rectificado. Por otro lado, también se plantea la utilización la metodología general para estimar fuera de máquina la energía específica de rectificado que puede ayudar a seleccionar la muela y los parámetros de rectificado por adelantado. Sin embargo, una única red no es suficiente para abarcar todas las muelas y condiciones de rectificado existentes. Así, también se propone una metodología para generar redes ad-hoc seleccionando unos datos específicos de toda la base de datos. Para ello, se ha hecho uso de los algoritmos Fuzzy c-Means. Finalmente, hay que decir que los resultados obtenidos mejoran los existentes hasta ahora. Sin embargo, estos resultados no son suficientemente buenos para poder controlar el proceso. Así, se propone la utilización de las redes neuronales de impulsos. Al trabajar con impulsos, estas redes tienen inherentemente la capacidad de trabajar con datos temporales, lo que las hace adecuados para estimar valores que evolucionan con el tiempo. Sin embargo, estas redes solamente se usan para clasificación y no predicción de evoluciones temporales por la falta de métodos de codificación/decodificación de datos temporales. Así, en este trabajo se plantea una metodología para poder codificar en trenes de impulsos señales secuenciales y poder reconstruir señales secuenciales a partir de trenes de impulsos. Esto puede llevar a en un futuro poder utilizar las redes neuronales de impulsos para la predicción de secuenciales y/o temporales