Electricity production and consumption modeling through fuzzy logic

Olaru, Lorena M.; Gellért, Árpád; Fiore, Ugo; Palmieri, Francesco

doi:10.1002/int.22942

Cited by 8 publications

(4 citation statements)

References 34 publications

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“…et al worked on electricity production and consumption modeling with fuzzy logic. It resulted in an average absolute error of 67.82 across all data sets, while the estimator based on the ARIMA model and MLP resulted in errors of 198.27 and 211.07, respectively [33].…”

Section: Literature Reviewmentioning

confidence: 97%

Determination of Electricity Production by Fuzzy Logic Method

ÖZDEM,

DÜĞENCİ,

İPEK

2024

Academic Platform Journal of Engineering and Smart Systems

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With the increase in the need for electrical energy, production amount planning is of great importance in order not to experience restrictions in terms of use, to meet the required electricity production, and to evaluate the excess production efficiently. In this study, a generation forecasting model was created with the fuzzy logic method to determine the electricity generation strategy. The created model is aimed to determine the electrical energy that needs to be produced daily by using the previous day's production amount, temperature, and season data. Three separate sets of data were used to test the fuzzy logic model built using information from the General Directorate of Meteorology (GDM) and Energy Markets Operations Inc. (EMOI). Fuzzy Logic was used to predict the data and the accuracy rates were found to be high. An improvement was observed when the accuracy rates were compared with the accuracy rates obtained in the Multiple Linear Regression Model. The accuracy rates of the model were initially examined using the Fuzzy Logic approach on weekdays and weekends, followed by a seasonal analysis and an assessment of the model's performance. As a result of the analysis, it was observed that the model worked with high accuracy in the autumn season and on weekend days.

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Section: Literature Reviewmentioning

confidence: 97%

Determination of Electricity Production by Fuzzy Logic Method

ÖZDEM,

DÜĞENCİ,

İPEK

2024

Academic Platform Journal of Engineering and Smart Systems

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“…Non-fuzzy prediction fve diferent series whose prediction could be interesting: (1) hourly (actual data), ( 2) morning [8,15]h, (3) evening [16,22]h, (4) night [23,7]h, and ( 5) daily (accumulated consumption).…”

Section: Data Collectionmentioning

confidence: 99%

“…Since this problem has in nature historical-oriented data, i.e., we always attempt to fnd dependencies between past values to model future ones, most of the authors employ time-series techniques to handle it. Plus, a variant that is gaining in popularity is the combination of fuzzy logic with time-series methods [2,[10][11][12][13][14][15][16].…”

Section: Introductionmentioning

confidence: 99%

Application of Fuzzy and Conventional Forecasting Techniques to Predict Energy Consumption in Buildings

Cabrera,

Ruiz,

Criado-Ramón

et al. 2023

International Journal of Intelligent Systems

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This paper presents the implementation and analysis of two approaches (fuzzy and conventional). Using hourly data from buildings at the University of Granada, we have examined their electricity demand and designed a model to predict energy consumption. Our proposal was conducted with the aid of time series techniques as well as the combination of artificial neural networks and clustering algorithms. Both approaches proved to be suitable for energy modelling although nonfuzzy models provided more variability and less robustness than fuzzy ones. Despite the relatively small difference between fuzzy and nonfuzzy estimates, the results reported in this study show that the fuzzy solution may be useful to enhance and enrich energy predictions.

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“…In several researches, a variety of forecasting techniques based on data mining were used to estimate future power or energy consumption. Multiple linear regression approach (Bianco et al., 2013; Ekonomou, 2010; Kialashaki & Reisel, 2014; Panklib et al., 2015), fuzzy logic (Ali et al., 2016; Islas et al., 2021; Kucukali & Baris, 2010; Olaru et al., 2022; Zahedi et al., 2013), autoregressive forecasting methods (Guefano et al., 2021; Kaytez, 2020; Nawaz et al., 2014; Nepal et al., 2020; Ozturk & Ozturk, 2018), support vector regression methods (Ekonomou, 2010; Hong & Fan, 2019; Kavaklioglu, 2011; Kaytez, 2020; Shao et al., 2020) and ANN (Ekonomou, 2010; Elbeltagi & Wefki, 2021; Günay, 2016; Kialashaki & Reisel, 2014; Li et al., 2018; Sarkar, Shankar, & Chaurasiya, 2015; Sarkar, Shankar, Thakur, et al., 2015; Torabi et al., 2019; Zahedi et al., 2013) have been extensively employed for this purpose. Several studies have been published over the past 10 years using a variety of approaches to anticipate the demand for electricity in various nations.…”

Section: Introductionmentioning

confidence: 99%

Modelling and forecasting India's electricity consumption using artificial neural networks

Bandyopadhyay,

Sarkar,

Hossain

et al. 2024

OPEC Energy Review

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Precise electricity forecasting is a pertinent challenge in effectively controlling the supply and demand of power. This is due to the inherent volatility of electricity, which cannot be stored and must be utilised promptly. Thus, this study develops a framework integrating canonical cointegrating regressions (CCR), time series artificial neural network (ANN) and a multilayer perceptron ANN model for analysing and projecting India's gross electricity consumption to 2030. Annual data for the years 1961–2020 have been collected for variables like gross domestic product (GDP), population, inflation GDP deflator (annual %), annual average temperature and electricity consumption. The study was conducted in three phases. In the first phase of the study, the CCR method was used to check the significance of the selected variables. In the second phase, the projected values of independent variables (GDP, population, inflation GDP deflator [annual %] and annual average temperature) were predicted using the time series ANN model. Finally, a multilayer perceptron ANN model with independent variables was used to forecast the gross electricity consumption in India by 2030. The result shows that the electricity consumption in India will increase by around 50% in the next 10 years, reaching over 1800 TWh in 2030. The proposed approach can be utilised to effectively implement energy policies, as an accurate prediction of energy consumption can help capture future demand.

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Electricity production and consumption modeling through fuzzy logic

Cited by 8 publications

References 34 publications

Determination of Electricity Production by Fuzzy Logic Method

Determination of Electricity Production by Fuzzy Logic Method

Application of Fuzzy and Conventional Forecasting Techniques to Predict Energy Consumption in Buildings

Modelling and forecasting India's electricity consumption using artificial neural networks

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