Electricity Usage Efficiency and Electricity Demand Modeling in the Case of Germany and the UK

Dudić, Branislav; Smolen, Jan; Kovac, Pavel; Savković, Borislav; Dudić, Zdenka

doi:10.3390/app10072291

Cited by 5 publications

(2 citation statements)

References 21 publications

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“…According to the results of this study, regression and multiple regression models are widely used in the field of electricity forecasting and are very efficient in long-term forecasting [20]. Dudic et al, 2020, used multiple regression to investigate the reason why Germany's electricity consumption did not grow in the last decade [21]. Another study in 2020, predicted the electricity consumption in India with regression.…”

Section: Introductionmentioning

confidence: 89%

Application of a hybrid model based on multiple linear regression -principle component analysis (MLR-PCA) for electricity export forecasting

2022

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International electricity trade as a strategic commodity plays a prominent role in the foreign trade market of countries.Electricity export forecasting leads to better production planning, supply security, blackouts reduction, and obligations fulfillment. This paper aimed to provide a model for electricity export forecasting. In this regard, electricity consumption in different consumer sectors, gas consumption, population, GDP, and electricity prices have been entered into the multiple regression model as predictor variables. Although R 2 =.976, F=66.110, and SIG<.05 indicate the model appropriateness, the high correlation between the predictor variables created collinearity. In other words, Tolerance, VIF (variance inflation factor), Eigenvalue and the Condition Index are less than .2, more than 10, close to zero, and more than 15 respectively. To solve this problem, two hybrid methods of Multiple Regression-First Difference Function and Multiple Regression-PCA have been used. In the first hybrid method (R 2 =.553) the Tolerance and VIF index still show the presence of collinearity. In the second hybrid method (R 2 =.936, F=169.9, SIG<.05) due to all the mentioned indicators, the collinearity has been completely resolved. So, the MLR-PCA method is the most appropriate model for electricity export forecasting. The data collected from Iran have been used to illustrate the model.

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Section: Introductionmentioning

confidence: 89%

Application of a hybrid model based on multiple linear regression -principle component analysis (MLR-PCA) for electricity export forecasting

2022

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“…Indeed, the efficient prediction of power consumption becomes more appropriate and accurate. Among the most methods that are used in the prediction and analysis of power consumption is a Linear Regression model (Bianco et al, 2013) (Safa et al, 2015) (Dudic et al, 2020).…”

Section: Introductionmentioning

confidence: 99%

Power Consumption Prediction of IoT Application Protocols Based on Linear Regression

Jeddou

Baïna

Abdallah

et al. 2021

International Journal of Artificial Intelligence and Machine Learning

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The advent of the Internet of Things (IoT) augurs new cutting-edge applications in modern life such as smart cities and smart grids. These applications require protocols more efficient for ensuring the reliability of data communications in the IoT networks. Many works state that IoT cannot meet their demands without application protocols improvement with Artificial Intelligence (AI) as IoT are expected to generate unprecedented traffic giving IoT researchers access to data that can help in studying and analyzing the demands and develop application protocols conceptions to meet the requirement of IoT applications. In literature, several works introduced AI in some layers of the TCP/IP model including wireless communication and routing. In this article, an evaluation of application protocols HTTP, MQTT, DDS, XMPP, AMQP, and CoAP has been presented; and subsequently, the power consumption prediction of MQTT and COAP based on the linear regression model is analyzed, in order to enhance data communications in IoT applications.

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Long-Term Forecasting of Electrical Loads in Kuwait Using Prophet and Holt–Winters Models

et al. 2020

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The rapidly increasing population growth and expansion of urban development are undoubtedly two of the main reasons for increasing global energy consumption. Accurate long-term forecasting of peak load is essential for saving time and money for countries’ power generation utilities. This paper introduces the first investigation into the performance of the Prophet model in the long-term peak load forecasting of Kuwait. The Prophet model is compared with the well-established Holt–Winters model to assess its feasibility and accuracy in forecasting long-term peak loads. Real data of electric load peaks from Kuwait powerplants from 2010 to 2020 were used for the electric load peaks, forecasting the peak load between 2020 and 2030. The Prophet model has shown more accurate predictions than the Holt–Winters model in five statistical performance metrics. Besides, the robustness of the two models was investigated by adding Gaussian white noise of different intensities. The Prophet model has proven to be more robust to noise than the Holt–Winters model. Furthermore, the generalizability test of the two models has shown that the Prophet model outperforms the Holt–Winters model. The reported results suggest that the forecasted maximum peak load is expected to reach 18,550 and 19,588 MW for the Prophet and Holt–Winters models by 2030 in Kuwait. The study suggests that the best months for scheduling the preventive maintenance for the year 2020 and 2021 are from November 2020 until March 2021 for both models.

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Electricity Usage Efficiency and Electricity Demand Modeling in the Case of Germany and the UK

Cited by 5 publications

References 21 publications

Application of a hybrid model based on multiple linear regression -principle component analysis (MLR-PCA) for electricity export forecasting

Application of a hybrid model based on multiple linear regression -principle component analysis (MLR-PCA) for electricity export forecasting

Power Consumption Prediction of IoT Application Protocols Based on Linear Regression

Long-Term Forecasting of Electrical Loads in Kuwait Using Prophet and Holt–Winters Models

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