Improving the Performance of Short-Term Load Forecast Using a Hybrid Artificial Neural Network and Artificial Bee Colony Algorithm Amélioration des performances de la prévision de la charge à court terme à l’aide d’un réseau neuronal artificiel hybride et d’un algorithme de colonies d’abeilles artificielles

Baesmat, Kamran Hassanpouri; Masodipour, Iman; Samet, Haidar

doi:10.1109/icjece.2021.3056125

Cited by 19 publications

(1 citation statement)

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“…These include areas such as medicine, 12,13 crime tracking systems, 14 and power load forecasting. 15,16 Researchers prefer artificial neural networks (ANN) in dealing with STLF problems, 17,18 owing to their strong nonlinear learning ability and fault tolerance. 19,20 However, many ANN-based gradient-based methods, such as backpropagation or other variants, have some limitations in the field of electric load forecasting.…”

Section: Introductionmentioning

confidence: 99%

Short‐term commercial load forecasting based on peak‐valley features with the TSA‐ELM model

Zhou

Zhu

et al. 2022

Energy Science & Engineering

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Commercial buildings are consuming an increasing amount of energy, and accurate load demand forecasting is critical for the reliable operation of power systems and the efficient use of resources. Therefore, in this paper, a shortterm commercial load forecasting model based on tunicate swarm algorithm (TSA) combined with an extreme learning machine (ELM) under peak-valley features is proposed as a research case for a shopping mall in Romania. This paper's overall structure is divided into two steps. In the first step, the 24-h day is divided into six periods by analyzing the daily load characteristics of the training set, and the peak and valley loads are obtained. The ELM optimized by TSA (TSA-ELM) algorithm is then used to forecast the peak and valley values of the test set one day ahead. In the second step, the actual load, peak, and valley for the previous week of historical load are chosen using the maximum information coefficient (MIC). Following that, the MIC ≥ 0.8 features are added to the TSA-ELM to achieve short-term commercial electricity load forecasting. The results show that the PV (Peak & Valley)-TSA-ELM model proposed in this paper has higher prediction accuracy compared with other models. Taking ELM as an example, compared with the traditional ELM, the mean absolute error, root mean square error, and mean absolute percentage error of PV-TSA-ELM are reduced by 20.59%, 20.13%, and 19.19% on average in the three commercial data sets. The proposed model is validated with an industrial data set, and ideal results are obtained, which verifies the effectiveness and superiority of the proposed method. K E Y W O R D S commercial electricity demand, extreme learning machine, peak and valley loads, short-term load forecasting, tunicate swarm algorithm

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