Solar energy is one of the most important renewable energies, with many advantages over other sources. Many parameters affect the electricity generation from solar plants. This paper aims to study the influence of these parameters on predicting solar radiation and electric energy produced in the Salt-Jordan region (Middle East) using long short-term memory (LSTM) and Adaptive Network-based Fuzzy Inference System (ANFIS) models. The data relating to 24 meteorological parameters for nearly the past five years were downloaded from the MeteoBleu database. The results show that the influence of parameters on solar radiation varies according to the season. The forecasting using ANFIS provides better results when the parameter correlation with solar radiation is high (i.e., Pearson Correlation Coefficient PCC between 0.95 and 1). In comparison, the LSTM neural network shows better results when correlation is low (PCC in the range 0.5–0.8). The obtained RMSE varies from 0.04 to 0.8 depending on the season and used parameters; new meteorological parameters influencing solar radiation are also investigated.

/pdf/solar-radiation-forecasting-by-pearson-correlation-using-3exg26hf.pdf

Solar Radiation Forecasting by Pearson Correlation Using LSTM Neural Network and ANFIS Method: Application in the West-Central Jordan

Electric vehicles in a short time will make up the majority of the fleet of vehicles used in general. This state of affairs will generate huge sets of data, which can be further investigated. The paper presents a methodology for the analysis of electric vehicle data, with particular emphasis on the energy consumption parameter. The prepared database contains data for 123 electric vehicles for analysis. Data analysis was carried out in a Python environment with the use of the dabl API library. Presentation of the results was made on the basis of data classification for continuous and categorical features vs. target parameters. Additionally, a heatmap Pearson correlation coefficient was performed to correlate the energy consumption parameter with the other parameters studied. Through the data classification for the studied dataset, it can be concluded that there is no correlation against energy consumption for the parameter charging speed; in contrast, for the parameters range and maximum velocity, a positive correlation can be observed. The negative correlation with the parameter energy consumption is for the parameter acceleration to 100 km/h. The methodology presented to assess data from electric vehicles can be scalable for another dataset to prepare data for creating machine learning models, for example.

/pdf/energy-consumption-of-electric-vehicles-analysis-of-selected-gdgh85ox.pdf

Energy Consumption of Electric Vehicles: Analysis of Selected Parameters Based on Created Database

This paper deals with visual evaluation of object distances using Soft-Computing based approaches and pseudo-3D standard low-cost sensor, namely the Kinect. The investigated technique points toward robots' vision and visual metrology of the robot's surrounding environment. The objective is providing the robot the ability of evaluating distances between objects in its surrounding environment. In fact, although presenting appealing advantages, the Kinect has not been designed for metrological aims. The investigated approach offers the possibility to use this low-cost pseudo-3D sensor for distance evaluation avoiding 3D feature extraction and thus exploiting the simplicity of only 2D image' processing. Experimental results show the viability of the proposed approach and provide comparison between different machine learning techniques as Adaptive-network-based fuzzy inference (ANFIS), Multi-layer Perceptron (MLP), Support vector regression (SVR), Bilinear interpolation.

http://ieeexplore.ieee.org/iel7/7523853/7532280/07533407.pdf

Learning-based distance evaluation in robot vision: A comparison of ANFIS, MLP, SVR and bilinear interpolation models

An element-scaling-revising method (ESRM) for magnetic flux leakage signal analysis

Deep learning based long-term global solar irradiance and temperature forecasting using time series with multi-step multivariate output

This paper deals with visual evaluation of object distances using Soft-Computing based approaches and

pseudo-3D standard low-cost sensor, namely the Kinect. The investigated technique points toward robotsâ

vision and visual metrology of the robotâs surrounding environment. The objective is providing the robot

the ability of evaluating distances between objects in its surrounding environment. In fact, although

presenting appealing advantages, the Kinect has not been designed for metrological aims. The investigated

approach offers the possibility to use this low-cost pseudo-3D sensor for distance evaluation avoiding 3D

feature extraction and thus exploiting the simplicity of only 2D imageâ processing. Experimental results

show the viability of the proposed approach and provide comparison between different machine learning

techniques as Adaptive-network-based fuzzy inference (ANFIS), Multi-layer Perceptron (MLP), Support

vector regression (SVR), Bilinear interpolation.

Learning-based Distance Evaluation in Robot Vision

The paper deals with visual objects' distance evaluation using Soft-Computing based approaches and pseudo-3D standard low-cost sensor, namely the Kinect. The investigated technique points toward robots' vision and the robot's surrounding environment metrology. Experimental results showing viability of the proposed approach, providing comparison with geometrical distance evaluation, are reported.

Soft-computing based fast visual objects' distance evaluation for robots' vision

The present paper describes a dual approach using pseudo-3D vision for Machine-Awareness in indoor environment. Provided by color and depth cameras of the a Kinect system, the aforementioned duality presents an appealing solution for robots' 3D-vision. Placing the human-robot and in a more general way the human-machine interactions as a key outcome of the expected visual Machine-Awareness, the proposed vision-system aims proffering the machine the self-reliance in awareness about the surrounding environment in which the machine is supposed to evolve. Blend pseudo-3D vision and salient objects' detection algorithm, the investigated approach seeks an autonomous detection of relevant items in 3D environment. The pseudo-3D perception leads to reducing computational complexity inborn to the 3D vision context into a 2D computational task by processing 3D visual information within a 2D-images' framework. The statistical foundation of the investigated approach proffers it a solid and comprehensive theoretical basis, holding out a bottom-up nature making the issued system unconstrained regarding prior hypothesis. We provide experimental results validating the proposed system.

Hossam Fraihat

Papers

Solar Radiation Forecasting by Pearson Correlation Using LSTM Neural Network and ANFIS Method: Application in the West-Central Jordan

Learning-based distance evaluation in robot vision: A comparison of ANFIS, MLP, SVR and bilinear interpolation models

Learning-based Distance Evaluation in Robot Vision

Soft-computing based fast visual objects' distance evaluation for robots' vision

Machine-awareness in indoor environment: A pseudo-3D vision-based approach combining multi-resolution visual information