Electricity Load Forecasting Using Data Mining Technique

Razak, Intan Azmira binti Wan Abdul; Majid, Shah; Aras, Mohd Shahrieel bin Mohd.; Ahmad, Arfah

doi:10.5772/48657

Cited by 4 publications

(1 citation statement)

References 8 publications

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“…In the error result analysis, the ARIMAX method produce a more accurate forecast when compared with machine learning-based regression as it has the least MAPE in each of the ten selected smart meters (Ding et al, 2013). Razak et al (2012) forecasted load by developing five different technique of SARIMA for different days of the week. The data from Peninsular Malaysia was used as the input to the developed techniques.…”

Section: Related Workmentioning

confidence: 99%

Performance Evaluation of Different Membership Function in Fuzzy Logic Based Short-Term Load Forecasting

Ibrahim¹,

Owonikoko²,

Abdulkarim³

et al. 2021

JST

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A mismatch between utility-scale electricity generation and demand often results in resources and energy wastage that needed to be minimized. Therefore, the utility company needs to be able to accurately forecast load demand as a guide for the planned generation. Short-term load forecast assists the utility company in projecting the future energy demand. The predicted load demand is used to plan ahead for the power to be generated, transmitted, and distributed and which is crucial to power system reliability and economics. Recently, various methods from statistical, artificial intelligence, and hybrid methods have been widely used for load forecasts with each having their merits and drawbacks. This paper investigates the application of the fuzzy logic technique for short-term load forecast of a day ahead load. The developed fuzzy logic model used time, temperature, and historical load data to forecast 24 hours load demand. The fuzzy models were based on both the trapezoidal and triangular membership function (MF) to investigate their accuracy and effectiveness for the load forecast. The obtained low Mean Absolute Percentage Error (MAPE), Mean Forecast Error (MFE), and Mean Absolute Deviation (MAD) values from the forecasted load results showed that both models are suitable for short-term load forecasting, however the trapezoidal MF showed better performance than the triangular MF.

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Section: Related Workmentioning

confidence: 99%

Performance Evaluation of Different Membership Function in Fuzzy Logic Based Short-Term Load Forecasting

Ibrahim¹,

Owonikoko²,

Abdulkarim³

et al. 2021

JST

View full text Add to dashboard Cite

show abstract

A hybrid predictive model for high-frequency and multi-periodic data in call center of online travel agency

Hou¹,

Gong²,

Nie³

et al. 2018

2018 IEEE International Conference on Big Data (Big Data)

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Analytical, Dynamic, and Interactive Platform for Generation and Managing Predictive Models Focused on Energy Sector

Romero

Ochoa

2022

Journal of Electrical and Computer Engineering

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Ensuring the supply of electricity in a reliable and safe way is not an easy task, especially when considering renewable and clean energy generated with wind turbines given the intermittency or variability of the wind; also considering different time horizons increases complexity. Mexico has great potential for wind energy in the Eastern region and, to meet this challenge, a platform capable of generating forecast models automatically through mathematical techniques and artificial intelligence and managing them is proposed aimed at providing support based on knowledge and presenting the information graphically through a flexible dashboard, which is customizable and accessible when and where required. In this investigation, components related to the generation of electrical energy in this area are identified and a centralized system is proposed, with information segmentation, management of 3 user profiles, 6 KPIs, 5 configurable parameters, 7 different forecast models using statistical techniques, support vector machines, and automatic and deep learning, with 2 ways of visualization, to carry out analyses at 3 different time horizons. It is built in a modular way with free and open-source software. The results in the energy sector show that it allows focusing on priority activities avoiding rework, ensures reliability and completeness, is scalable, avoids duplication, allows resources to be shared, responds quickly to hypotheses, and has a global and summarized view of relevant data according to the interested party for different periods of time in an agile way, reducing times and offering support to the decision maker.

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Electricity Load Forecasting Using Data Mining Technique

Cited by 4 publications

References 8 publications

Performance Evaluation of Different Membership Function in Fuzzy Logic Based Short-Term Load Forecasting

Performance Evaluation of Different Membership Function in Fuzzy Logic Based Short-Term Load Forecasting

A hybrid predictive model for high-frequency and multi-periodic data in call center of online travel agency

Analytical, Dynamic, and Interactive Platform for Generation and Managing Predictive Models Focused on Energy Sector

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