Submitted Successfully!
To reward your contribution, here is a gift for you: A free trial for our video production service.
Thank you for your contribution! You can also upload a video entry or images related to this topic.
Version Summary Created by Modification Content Size Created at Operation
1
Cristian L Vidal-Silva
 -- 1103 2023-11-22 08:45:58 |
2 format correct
Catherine Yang
Meta information modification 1103 2023-11-22 08:49:02 |

Video Upload Options

Do you have a full video?
Send video materials Upload full video

Confirm

Are you sure to Delete?
Yes No
Cite
If you have any further questions, please contact Encyclopedia Editorial Office.
Rubio-León, J.; Rubio-Cienfuegos, J.; Vidal-Silva, C.; Cárdenas-Cobo, J.; Duarte, V. Fuzzy Time Series for Electricity Demand. Encyclopedia. Available online: https://encyclopedia.pub/entry/51894 (accessed on 07 May 2024).
Rubio-León J, Rubio-Cienfuegos J, Vidal-Silva C, Cárdenas-Cobo J, Duarte V. Fuzzy Time Series for Electricity Demand. Encyclopedia. Available at: https://encyclopedia.pub/entry/51894. Accessed May 07, 2024.
Rubio-León, José, José Rubio-Cienfuegos, Cristian Vidal-Silva, Jesennia Cárdenas-Cobo, Vannessa Duarte. "Fuzzy Time Series for Electricity Demand" Encyclopedia, https://encyclopedia.pub/entry/51894 (accessed May 07, 2024).
Rubio-León, J., Rubio-Cienfuegos, J., Vidal-Silva, C., Cárdenas-Cobo, J., & Duarte, V. (2023, November 22). Fuzzy Time Series for Electricity Demand. In Encyclopedia. https://encyclopedia.pub/entry/51894
Rubio-León, José, et al. "Fuzzy Time Series for Electricity Demand." Encyclopedia. Web. 22 November, 2023.
Fuzzy Time Series for Electricity Demand
Edit
This entry is adapted from the peer-reviewed paper 10.3390/math11173667

A time series is a succession of data ordered chronologically in defined time intervals. The data may be evenly spaced, such as the record of daily solar generation from a photovoltaic plant, or it may be different, such as the number of annual earthquakes in a defined area. This type of representation offers many advantages because its analysis allows us to discover underlying relationships in the data, which can be from various time series or within the data itself. These can be used to extrapolate behavior in the past, during periods of data loss, and in the future.

electricity ENTSO-E fuzzy logic and models machine learning

1. Introduction

Managing the energy produced to support industries and various human activities is highly relevant nowadays. Companies in the electricity markets of each country analyze the generation, transmission, and distribution of energy to meet the energy needs of various sectors and industries. Electrical markets emerge to economically analyze everything related to energy generation, transmission, and distribution. The demand for electric energy is crucial in determining the amount of energy needed to meet the requirements of an individual or a group of consumers.

Electric power has been one of the most significant driving forces for humanity since the late 18th century (see [1]). Currently, every industry relies on electricity, creating a significant need to effectively manage the energy generated in order to sustain and advance all human activities that depend on its use. Electrical markets emerge to economically analyze all aspects of energy generation, transmission, and distribution (see [2]). One purpose of electrical markets is to satisfy all the energy needs of each sector and industry [3]. An essential variable for understanding this behavior is the demand for electric energy [4]. This information indicates the amount of energy required for an entity or a series of consumers to meet their needs [5].
Since human activities are influenced by various external factors, the demand for energy is not exempt from these influences. Analyzing and understanding energy demand is essential for the development of the energy sector [6][7]. That is highly relevant for the field of energy generation [8], where this last activity is still carried out for most non-renewable resources. Oil (32.89%), coal (29.16%), and natural gas (23.40%) are the three most used energy sources in the world [9]. This characteristic shows the necessity of developing models that enable demand forecasting. By doing so, it would facilitate improved management of energy generation and consumption.
When analyzing the electricity demand as a time series and observing its evolution over time, the researchers can discover interesting patterns and behaviors [10]. The growth over the years can be attributed to the development of industries, population, technology, and economic development (see Figure 1). On the other hand, if the demand is analyzed with values recorded per hour, an increase is observed during the day and a reduction is observed at night. This indicates a close relationship between this variable and the development of work and daily activities [11]. However, when analyzing the demand during this recent period, some records do not adhere to this pattern on multiple occasions and even exceed the maximum values recorded in previous days. As previously mentioned, the demand depends to a large extent on the development of human activities, which are influenced by a wide range of factors. These factors often exhibit stochastic behaviors, such as electrical system failures, events with high attendance, the economic evolution of the industry, and climate changes, among others. Hence, the demand often exhibits random behavior, making it challenging to analyze when developing forecast models [12].
Figure 1. Increase in energy demand, population, electric consumption and CO2 from 1970 to 2015. (a) The increase in energy demand and Spanish GDP, an economic value, shows how demand increases as the economy grows [13]. (b) Evolution of population, electricity consumption, and CO2 emissions in the last decades [14].
Due to these characteristics and the significance of demand, numerous systems have been developed to forecast this variable using various techniques, including supervised machine learning, deep learning, and autoregressive systems [15][16][17]. Among all the regression models, the utilization of fuzzy series has shown superior performance in this field [18][19]. Besides their diversity, these models facilitate the smooth integration of information from related variables. This is because their training process thoroughly analyzes the relationships between the processed variables [20]. 

2. Time Series

2.1. Fuzzy Logic and Fuzzy Time Series

Fuzzy logic is a form of paraconsistent logic (a logic system that handles contradictions in a weakened manner) that does not categorize all statements as completely true or false. This is the primary distinction from classical logic (see refs. [21][22]). Fuzzy logic allows for an interesting approach to decision problems because, in the real world, it is impossible to abstract everything into a binary system [23]. For example, let’s consider a dataset that records the heights of people in order to determine who is tall or short (see Figure 2). The researchers can use the value of 1.70 m as the dividing point: individuals with a height equal to or greater than 1.70 m are considered tall, while those with a height lower than 1.70 m are considered short. With standard logic, it is assumed that all the data will fall into one of these categories; however, would it be accurate to classify someone with a height of 1.69 m as small or 1.71 m as tall?
Figure 2. Differences between the association of the height to the sets of tall and short through classical logic (a) and fuzzy logic (b), the x-axis in both examples corresponds to the height. In contrast, the y-axis corresponds to the membership degree.
Fuzzy logic allows for the establishment of a degree of membership among the defined sets. This means that variables can belong to more than one set [24]. Fuzzy logic operates on fuzzy sets, which have values in a range of [0, 1] instead of binary terms. These values are determined by the membership function of a set for each element that belongs to the universe of discourse [25]. Thus, a fuzzy set A is characterized by its membership function in Equation (1).
As its name indicates, a fuzzy time series corresponds to a time series that utilizes fuzzy logic to transform each value of the series into elements that belong to fuzzy sets [26]. A time series in the fuzzy domain further enhances the analysis of the series by applying fuzzy set relationship analysis to the original data [27].

2.2. Universe of Discourse

In fuzzy time series, the universe of discourse represents the range of values that certain time series elements can take, which in turn represents a specific phenomenon [28]. For example, if there is a time series with values 𝑌∈ℝ, the universe of discourse U would be defined as 𝑈=[min(𝑌)−𝑚,max(𝑌)+𝑚], where m represents a margin that allows for the inclusion

References

  1. Erenoglu, A.K.; Erdinç, O.; Taşcıkaraoğlu, A. History of Electricity. In Pathways to a Smarter Power System; Elsevier: Amsterdam, The Netherlands, 2019; pp. 1–27.
  2. He, W.; King, M.; Luo, X.; Dooner, M.; Li, D.; Wang, J. Technologies and economics of electric energy storages in power systems: Review and perspective. Adv. Appl. Energy 2021, 4, 100060.
  3. Wang, S.; Sun, L.; Iqbal, S. Green financing role on renewable energy dependence and energy transition in E7 economies. Renew. Energy 2022, 200, 1561–1572.
  4. Filippov, S.; Malakhov, V.; Veselov, F. Long-term energy demand forecasting based on a systems analysis. Therm. Eng. 2021, 68, 881–894.
  5. Twenergy. La Demanda eléCtrica. Available online: https://twenergy.com/eficiencia-energetica/como-ahorrar-energia-casa/la-demandaelectrica-953/ (accessed on 10 August 2023).
  6. Abbasi, K.R.; Hussain, K.; Abbas, J.; Adedoyin, F.F.; Shaikh, P.A.; Yousaf, H.; Muhammad, F. Analyzing the role of industrial sector’s electricity consumption, prices, and GDP: A modified empirical evidence from Pakistan. Aims Energy 2021, 9, 29–49.
  7. Laimon, M.; Mai, T.; Goh, S.; Yusaf, T. Energy sector development: System dynamics analysis. Appl. Sci. 2019, 10, 134.
  8. Ahmad, T.; Zhang, D. A critical review of comparative global historical energy consumption and future demand: The story told so far. Energy Rep. 2020, 6, 1973–1991.
  9. Arriols, E. Cuales son las Fuentes de Energía más Utilizadas en el Mundo. Available online: https://www.ecologiaverde.com/cuales-son-las-fuentes-de-energia-mas-utilizadas-en-el-mundo-1426.html (accessed on 10 August 2023).
  10. Niu, Z.; Wu, J.; Liu, X.; Huang, L.; Nielsen, P.S. Understanding energy demand behaviors through spatio-temporal smart meter data analysis. Energy 2021, 226, 120493.
  11. Moral-Carcedo, J.; Pérez-García, J. Time of day effects of temperature and daylight on short term electricity load. Energy 2019, 174, 169–183.
  12. Koot, M.; Wijnhoven, F. Usage impact on data center electricity needs: A system dynamic forecasting model. Appl. Energy 2021, 291, 116798.
  13. Prevención, I. El Dato del Día: Evolución del Consumo de Energía en los últimos Cincuenta Años. Available online: https://bit.ly/3Hyiq45 (accessed on 10 August 2023).
  14. Forecasting, A.E. La Eficiencia Energética en España o Cómo ha Cambiado el Uso de la Energía Desde el Récord de Demanda de 2007. Enero 2021. Available online: https://bit.ly/3qNMkKO (accessed on 10 August 2023).
  15. Almaghrebi, A.; Aljuheshi, F.; Rafaie, M.; James, K.; Alahmad, M. Data-driven charging demand prediction at public charging stations using supervised machine learning regression methods. Energies 2020, 13, 4231.
  16. Aslam, M.S.; Ghazal, T.M.; Fatima, A.; Said, R.A.; Abbas, S.; Khan, M.A.; Siddiqui, S.Y.; Ahmad, M. Energy-efficiency model for residential buildings using supervised machine learning algorithm. Intell. Autom. Soft Comput. 2021, 30, 881–888.
  17. Olu-Ajayi, R.; Alaka, H.; Sulaimon, I.; Sunmola, F.; Ajayi, S. Building energy consumption prediction for residential buildings using deep learning and other machine learning techniques. J. Build. Eng. 2022, 45, 103406.
  18. Song, C.; Xu, Z.; Song, C.; Xu, Z. Regression Analysis Models Under the Hesitant Fuzzy Environment. In Techniques of Decision Making, Uncertain Reasoning and Regression Analysis under the Hesitant Fuzzy Environment and Their Applications; Springer: Berlin/Heidelberg, Germany, 2021; pp. 83–124.
  19. Song, C.; Wang, L.; Xu, Z. An optimized logistic regression model based on the maximum entropy estimation under the hesitant fuzzy environment. Int. J. Inf. Technol. Decis. Mak. 2022, 21, 143–167.
  20. Alagbe, V.; Popoola, S.I.; Atayero, A.A.; Adebisi, B.; Abolade, R.O.; Misra, S. Artificial intelligence techniques for electrical load forecasting in smart and connected communities. In Proceedings of the Computational Science and Its Applications–ICCSA 2019: 19th International Conference, Saint Petersburg, Russia, 1–4 July 2019; Part V 19. Springer: Berlin/Heidelberg, Germany, 2019; pp. 219–230.
  21. Goguen, J.A. L. A. Zadeh. Fuzzy Sets. Information and Control, Vol. 8, pp. 338?353. - L. A. Zadeh. Similarity Relations and Fuzzy Orderings. Information Sciences, Vol. 3, pp. 177–200. J. Symb. Log. 1973, 38, 656–657.
  22. Samonto, S.; Kar, S.; Pal, S.; Atan, O.; Sekh, A.A. Fuzzy logic controller aided expert relaying mechanism system. J. Frankl. Inst. 2021, 358, 7447–7467.
  23. Chandrasekaran, S.; Durairaj, S.; Padmavathi, S. A Performance evaluation of a fuzzy logic controller-based Photovoltaic-fed multi-level inverter for a three-phase induction motor. J. Frankl. Inst. 2021, 358, 7394–7412.
  24. Khater, A.A.; El-Nagar, A.M.; El-Bardini, M.; El-Rabaie, N.M. Online learning of an interval type-2 TSK fuzzy logic controller for nonlinear systems. J. Frankl. Inst. 2019, 356, 9254–9285.
  25. Zou, Y.; Yan, F.; Wang, X.; Zhang, J. An efficient fuzzy logic control algorithm for photovoltaic maximum power point tracking under partial shading condition. J. Frankl. Inst. 2020, 357, 3135–3149.
  26. Gautam, S.S.; Abhishekh. A novel moving average forecasting approach using fuzzy time series data set. J. Control. Autom. Electr. Syst. 2019, 30, 532–544.
  27. Najariyan, M.; Pariz, N. Stability and controllability of fuzzy singular dynamical systems. J. Frankl. Inst. 2022, 359, 8171–8187.
  28. Pattanayak, R.M.; Behera, H.S.; Panigrahi, S. A novel probabilistic intuitionistic fuzzy set based model for high order fuzzy time series forecasting. Eng. Appl. Artif. Intell. 2021, 99, 104136.
More
© Text is available under the terms and conditions of the Creative Commons Attribution (CC BY) license; additional terms may apply. By using this site, you agree to the Terms and Conditions and Privacy Policy.
Upload a video for this entry
Information
Subjects: Mathematics
Contributors MDPI registered users' name will be linked to their SciProfiles pages. To register with us, please refer to https://encyclopedia.pub/register :
José Rubio-León
,
José Rubio-Cienfuegos
,
Cristian Vidal-Silva
,
Jesennia Cárdenas-Cobo
,
Vannessa Duarte
View Times: 99
Revisions: 2 times (View History)
Update Date: 22 Nov 2023
Table of Contents
    1000/1000
    Hot Most Recent
    Feedback