Weather biased optimal delta model for short‐term load forecast

Uppal, Manish; Garg, V. K.; Kumar, Dinesh

doi:10.1049/iet-stg.2019.0331

Cited by 4 publications

(1 citation statement)

References 32 publications

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“…The results show that the outliers' pre-filtering increases the accuracy of EPF. To deal with India's illegal electricity market, an algorithm has been proposed in [25] to predict the next day's electricity load, and the performance of the proposed method has been confirmed at the seasonal level. In [26], a linear model for predicting long-term EP in the Danish market is presented.…”

Section: Introductionmentioning

confidence: 99%

The Investigation of Monthly/Seasonal Data Clustering Impact on Short-Term Electricity Price Forecasting Accuracy: Ontario Province Case Study

et al. 2022

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The transformation of the electricity market structure from a monopoly model to a competitive market has caused electricity to be exchanged like a commercial commodity in the electricity market. The electricity price participants should forecast the price in different horizons to make an optimal offer as a buyer or a seller. Therefore, accurate electricity price prediction is very important for market participants. This paper investigates the monthly/seasonal data clustering impact on price forecasting. To this end, after clustering the data, the effective parameters in the electricity price forecasting problem are selected using a grey correlation analysis method and the parameters with a low degree of correlation are removed. At the end, the long short-term memory neural network has been implemented to predict the electricity price for the next day. The proposed method is implemented on Ontario—Canada data and the prediction results are compared in three modes, including non-clustering, seasonal, and monthly clustering. The studies show that the prediction error in the monthly clustering mode has decreased compared to the non-clustering and seasonal clustering modes in two different values of the correlation coefficient, 0.5 and 0.6.

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Section: Introductionmentioning

confidence: 99%