Model Development for Strength Properties of Laterized Concrete Using Artificial Neural Network Principles

Awoyera, Paul O.; Akinmusuru, Joe O.; Krishna, A. Shiva; Gobinath, R.; Arunkumar, B.; Sangeetha, G.

doi:10.1007/978-981-15-0035-0_15

Cited by 18 publications

(6 citation statements)

References 16 publications

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“…In MLR, output variable (Y a ) is related linearly with multiple input variables like (X a 1 , X a 2 , • • • , X a n ). For a given input variables (X a 1 , X a 2 , • • • , X a n ), output Y will be predicted using Equation (7). Gradient descent optimization method has been used to find the values of (m 1 , m 2 , • • • , m n ) and c for the given inputs (X a ) and corresponding outputs (Y a ), such that the half mean distance between all the output data points and line represented by Equation ( 7) is minimum as shown in Figure 4.…”

Section: Multiple Linear Regression Model (Mlr)mentioning

confidence: 99%

“…Artificial Intelligence (AI) is an integral part of many fields, some of the main subparts of AI are machine learning and swarm intelligence. Machine learning has become an integral part in many fields like civil engineering applications [6,7], image processing [8] and time series data prediction [9]. Swarm intelligence was developed by taking inspiration from the swarming behavior of various natural systems and this is used to solve various optimization problems [10,11].…”

Section: Introductionmentioning

confidence: 99%

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Short Term Active Power Load Prediction on A 33/11 kV Substation Using Regression Models

et al. 2021

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Electric power load forecasting is an essential task in the power system restructured environment for successful trading of power in energy exchange and economic operation. In this paper, various regression models have been used to predict the active power load. Model optimization with dimensionality reduction has been done by observing correlation among original input features. Load data has been collected from a 33/11 kV substation near Kakathiya University in Warangal. The regression models with available load data have been trained and tested using Microsoft Azure services. Based on the results analysis it has been observed that the proposed regression models predict the demand on substation with better accuracy.

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Section: Multiple Linear Regression Model (Mlr)mentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

Short Term Active Power Load Prediction on A 33/11 kV Substation Using Regression Models

et al. 2021

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“…Artificial intelligence (AI) has become an integral part in many fields like waste generation [6,7], automobiles, international business, educational institution [8], civil engineering applications [9,10], medical [11], image processing [12] and also, electrical power system applications [13]. AI analyze the system and make prediction based on previous available data.…”

Section: Introductionmentioning

confidence: 99%

Electric power load forecasting on a 33/11 kV substation using artificial neural networks

Veeramsetty

Deshmukh

2020

SN Appl. Sci.

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Estimation of electric power load on electric power substation is an essential task for system operator in order to operate the system in a reliable and optimal manner. In this paper, machine learning with artificial neural network is used for forecasting the load at a particular hour of the day on an electric power substation. Historical load data at each hour of the day for the period from September-2018 to November-2018 is taken from 33/11 kV substation near Kakatiya University in Warangal. A new artificial neural network architecture is developed based on the approach used to forecast the load. The developed model is simulated in MATLAB with available historical data to forecast the load on 33/11 kV electric power substation. Based on the analysis it is observed that the proposed architecture forecasts the load with better accuracy.Keywords Artificial neural networks · Electric power load forecasting · Machine learning · Mean square error · Mean absolute percentage error List of symbols L(D, t) Load at Dth day and tth hour L(D, t − 1) Load at Dth day and (t − 1)th hour L(D, t − 2) Load at Dth day and (t − 2)th hour L(D, t − 3) Load at Dth day and (t − 3)th hour L(t, D − 1) Load at (D − 1)th day and tth hour L(t, D − 2) Load at (D − 2)th day and tth hour L(t, D − 3) Load at (D − 3)th day and tth hour L(t, D − 4) Load at (D − 4)th day and tth hour MAPE Mean absolute percentage error MSE Mean square error y Target i Actual output y Predicted i Predicted output m Number of samples R Regression coefficient * Venkataramana Veeramsetty,

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“…Artificial intelligence (AI) has become an integral part of many fields, such as civil engineering applications, 7,8 image processing, 9 and the medical sector 10 . AI techniques can help the power system operator to trade electricity at a maximum profit by predicting electrical load and electricity prices.…”

Section: Introductionmentioning

confidence: 99%

Short‐term electric power load forecasting using factor analysis and long short‐term memory for smart cities

Veeramsetty¹,

Chandra²,

Salkuti

2020

Circuit Theory & Apps

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Electric load estimation is an important activity for electrical power system operators to operate the system stably and optimally. This paper develops a machine learning model with a long short-term memory and a factor analysis to predict the load at a specific hour of the day on an electrical power substation. Historical load data from the 33-/11-kV substation near Kakatiya University in Warangal are taken at each hour of the day for the period from September 2018 to November 2018. A new long short-term memory architecture with factor analysis is being designed based on the approach used to predict substation loads by simulation in Microsoft Azure Notebooks. Based on the study, it was found that the proposed design predicts loads with good accuracy.

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Model Development for Strength Properties of Laterized Concrete Using Artificial Neural Network Principles

Cited by 18 publications

References 16 publications

Short Term Active Power Load Prediction on A 33/11 kV Substation Using Regression Models

Short Term Active Power Load Prediction on A 33/11 kV Substation Using Regression Models

Electric power load forecasting on a 33/11 kV substation using artificial neural networks

Short‐term electric power load forecasting using factor analysis and long short‐term memory for smart cities

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