Predictor Analysis for Electricity Price Forecasting by Multiple Linear Regression

Ülgen, Toygar; Poyrazoglu, Gokturk

doi:10.1109/speedam48782.2020.9161866

Cited by 10 publications

(5 citation statements)

References 10 publications

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“…In the article Multiple linear regression model for predicting bidding price, it shows accuracy estimated from statistics of validation data r square, MAPE is used as estimators of model [9]. Multiple linear regression is one of the best machine learning algorithms for finding predictions of values and shows why it is the best algorithm to use [2]. The similar findings are that multiple linear regression provides more accurate values or predictions and is capable of capturing a decent amount of variations [10].…”

Section: Discussionmentioning

confidence: 92%

“…The attributes are the central values to be calculated to show prediction-the attributes like location, date-time, passenger count, and existing fare amount [2]. The existing fare amount should be changed or updated through the program, and the fare amount is updated through weather conditions, day or night, etc.…”

Section: Introductionmentioning

confidence: 99%

“…The forecast value is derived from the study of independent factors. Then it was utilized to predict corn [2] better. In the investigation of Multiple linear regression predictor analysis for electricity price forecasting, error rates are considerably reduced using multiple linear regression.…”

Section: Introductionmentioning

confidence: 99%

See 2 more Smart Citations

Ola Data Analysis for Dynamic Price Prediction Using Multiple Linear Regression and Random Forest Regression

Tarun

Sriramya

2022

Advances in Parallel Computing Algorithms, Tools and Paradigms

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This research aims to create the most efficient and accurate cab fare prediction system using two machine learning algorithms, the Multiple linear Regression algorithm and the random forest algorithm, and compare parameters r-square, Mean Square Error (MSE), Root MSE, and RMSLE values to evaluate the efficiency of two machine learning algorithm. Considering Multiple linear Regression as group 1 and random forest algorithms as implemented, the 2 group process was to predict prices and get the best accuracy to compare algorithms. The algorithm should be efficient enough to produce the exact fare amount of the trip before the trip starts. The sample size for implementing this work was N=10 for each group considered. The sample size calculation was done with clincle. The pretest analysis was kept at 80%. The sample size is estimated using G-power. Based on the statistical analysis significance value for calculating r-squared, MSE was 0.945 and 0.266(p>0.05), respectively. The Multiple linear algorithms give a slightly better accuracy rate with a mean r-squared percentage of 71.69%, and the Random forest algorithm has a mean r-square of 71.29%. Through this, prediction is made for online booking of cabs or taxis, and the Multiple linear algorithms give a slightly better r-squared value than the Random forest algorithm.

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Section: Discussionmentioning

confidence: 92%

Section: Introductionmentioning

confidence: 99%

See 1 more Smart Citation

Ola Data Analysis for Dynamic Price Prediction Using Multiple Linear Regression and Random Forest Regression

Tarun

Sriramya

2022

Advances in Parallel Computing Algorithms, Tools and Paradigms

View full text Add to dashboard Cite

show abstract

“…In recent years, many studies (Mirakyan, 2017;Bento et al, 2018;Kuo & Huang, 2018;Uğurlu et al, 2018;Xie et al, 2018;Zhou, 2019;Guo et al, 2020;Huang et al, 2020;Qiao & Yang, 2020;Li & Becker, 2021;Tschora et al, 2022;Yang et al, 2022) have been conducted on electricity price prediction with various statistics. Moreover, many studies (Cervone, 2014;Marcos et al, 2019;Ulgen & Poyrazoğlu, 2020;Matsumoto & Endo, 2021;Rajan & Chandrakala, 2021) have been conducted by artificial intelligence models that can learn the complex structure of electricity prices. In addition, studies in which volatility estimation for electricity prices was made (Zareipour et al, 2007;Chan et al, 2008;Karakatsani & Bunn, 2010;Ciarreta & Zarraga, 2016;Dong et al, 2019;Terzic et al, 2021;Niza et al, 2022) come to the fore in the literature.…”

Section: Literature Reviewmentioning

confidence: 99%

Testing the Factors Affecting Intraday Market Electricity Prices by Connectedness Approach

2023

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This study aims to dynamically analyse the relationship between intraday market electricity prices and day-ahead market electricity prices and the amount of electricity generated based on the primary energy resource in Turkey. In this context, the data set consisting of electricity prices in the day-ahead market, electricity prices in the day-ahead market, and electricity generation amount based on primary energy resources, covering the period from 1 January 2018 to 19 June 2022, was analysed with TVP-VAR. Findings reveal that the relationship between variables changes over time and is affected by global events. Furthermore, it has been determined that the intraday market has moved from a general receiver of volatility to a general transmitter in the post-Covid 19 period.

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“…Several other studies have also stated that MRL can carry out the process of analyzing factors that affect changes in the price of gold in the future [14]. In parallel research also carried out in the process of predicting electricity prices, stating that the MRL method can analyze variables that can affect the predicted output results [15]. For this reason, MRL can take a statistical approach by looking at the relationship of the variables used in influencing the output [16].…”

Section: Introductionmentioning

confidence: 99%

Implementation multiple linear regresion in neural network predict gold price

Yanto

Sanjaya

Yulasmi

et al. 2021

IJEECS

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<p>The movement of gold prices in the previous period was crucial for investors. However, fluctuations in gold price movements always occur. The problem in this study is how to apply multiple linear regression (MRL) in predicting artificial neural networks (ANN) of gold prices. MRL is mathematical calculation technique used to measure the correlation between variables. The results of the MRL analysis ensure that the network pattern that is formed can provide precise and accurate prediction results. In addition, this study aims to develop a predictive pattern model that already exists. The results of the correlation test obtained by MRL provide a correlation of 62% so that the test results are said to have a significant effect on gold price movements. Then the prediction results generated using an ANN has a mean squared error (MSE) value of 0.004264%. The benefits obtained in this study provide an overview of the gold price prediction pattern model by conducting learning and approaches in testing the accuracy of the use of predictor variables.</p>

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Predictor Analysis for Electricity Price Forecasting by Multiple Linear Regression

Cited by 10 publications

References 10 publications

Ola Data Analysis for Dynamic Price Prediction Using Multiple Linear Regression and Random Forest Regression

Ola Data Analysis for Dynamic Price Prediction Using Multiple Linear Regression and Random Forest Regression

Testing the Factors Affecting Intraday Market Electricity Prices by Connectedness Approach

Implementation multiple linear regresion in neural network predict gold price

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