Short term load forecasting using fuzzy logic control

K, Anoop; Kanchana, K.

doi:10.1109/icpcsi.2017.8392272

Cited by 6 publications

(1 citation statement)

References 5 publications

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“…The fuzzy logic system, which performs on the IF-THEN logic, has been reported in the literature (Alrizq and Doncker, 2018;Alam and Ali, 2020a) to have performed well in nonlinear conditions. Moreover, temperature and historical data are the most considered inputs for the fuzzy systems that are proposed in the literature (Anoop and Kanchana, 2017;Shao et al, 2018), although performance can still be improved further by increasing the input number. However, limited input numbers are considered because as the input number increases, the fuzzy rules also increase, which makes the system slower.…”

Section: Introductionmentioning

confidence: 99%

Residential load forecasting by a PSO-tuned ANFIS2 method considering the COVID-19 influence

Alam,

Ali

2024

Front. Energy Res.

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The most important feature of load forecasting is enabling the building management system to control and manage its loads with available resources ahead of time. The electricity usage in residential buildings has increased during the COVID-19 period, as compared to normal times. Therefore, the performance of forecasting methods is impacted, and further tuning of parameters is required to cope with energy consumption changes due to COVID-19. This paper proposes a new adaptive neuro-fuzzy 2 inference system (ANFIS2) for energy usage forecasting in residential buildings for both normal and COVID-19 periods. The particle swarm optimization (PSO) method has been implemented for parameter optimization, and subtractive clustering is used for data training for the proposed ANFIS2 system. Two modifications in terms of input and parameters of ANFIS2 are made to cope with the change in the consumption pattern and reduce the prediction errors during the COVID-19 period. Simulation results obtained by MATLAB software validate the efficacy of the proposed ANFIS2 in residential load forecasting during both normal and COVID-19 periods. Moreover, the performance of the proposed method is better than that of the existing adaptive neuro-fuzzy inference system (ANFIS), long short-term memory (LSTM), and random forest (RF) approaches.

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