Short-Term Load Forecasting Models: A Review of Challenges, Progress, and the Road Ahead

Akhtar, Saima; Shahzad, Sulman; Zaheer, Asad; Ullah, Hafiz Sami; Kilic, Heybet; Gono, Radomir; Jasiński, Michał; Leonowicz, Zbigniew

doi:10.3390/en16104060

Cited by 37 publications

(12 citation statements)

References 122 publications

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“…In addition, the FO can assist in measuring the appropriateness of COOT best (17). The Gbest Score and its position Gbest p are defined as follows (18):…”

Section: (C) Attackingmentioning

confidence: 99%

“…In recent research articles, different optimization approaches have been used to find the OADG problems. The aimed ideas are to reduce the voltage deviation (VD) [7][8][9][10][11], lessen power losses [12], improve the stability index [13], upgrade the stability of transients [14][15][16], enhance reliability [17][18][19][20][21] and drop in greenhouse gas emission [22][23][24]. In distribution generation, power is produced using a small generating system near the consumer premises.…”

Section: Introductionmentioning

confidence: 99%

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Optimal allocation of distributed generation in meshed power networks: A metaheuristic approach

Khan,

Waheed,

Shahzad

et al. 2024

IET Renewable Power Gen

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This paper introduces a novel metaheuristic technique, a COOT‐based algorithm, to determine the optimal Distributed Generation (DG) allocation within a loop‐configured network. This method significantly narrows the optimization gap by leveraging a COOT‐based algorithm, ensuring accelerated convergence and resultant global optima. The core incentive for employing this technique is to substantially mitigate power loss, curtail voltage deviation, and bolster system stability in a loop distribution network. To attain optimal outcomes, the elaborated COOT and improved grey wolf optimization improved grey wolf optimization (IGWO) algorithms were executed on IEEE bus‐33 and 69 mesh distribution networks (MDNs) under varying power factors. The derived mathematical results effectively underscore accomplishing the stipulated objectives: a marked reduction in voltage deviation and power loss coupled with an augmentation in system stability. Notably, at unity, incorporating DGs resulted in a paramount reduction in power loss, attaining a decrease of 78% and 85% for bus‐33 and 69 MDNs, respectively. Moreover, an impressive decrease in power loss by 94% and 98% was observed at the optimal power factor for both MDNs. A comparative evaluation of the results accentuates that the proposed COOT and IGWO algorithms eclipse other documented research in performance, showcasing superior efficiency on a techno‐economic basis.

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“…In addition, the FO can assist in measuring the appropriateness of COOT best (17). The Gbest Score and its position Gbest p are defined as follows (18):…”

Section: (C) Attackingmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

Optimal allocation of distributed generation in meshed power networks: A metaheuristic approach

Khan,

Waheed,

Shahzad

et al. 2024

IET Renewable Power Gen

Self Cite

View full text Add to dashboard Cite

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“…Power load forecasting can be categorized into four distinct time horizons: long-term (Şeker, 2022), medium-term (Han et al, 2022), shortterm (Niu et al, 2022), and very short-term (Li et al, 2023). Short-Term Power Load Forecasting (STPLF) is about predicting power demand for daily and weekly operational plans which includes day-ahead predictions up to the coming week (Akhtar et al, 2023).…”

Section: Motivationmentioning

confidence: 99%

Short-term power load forecasting for integrated energy system based on a residual and attentive LSTM-TCN hybrid network

Li,

et al. 2024

Front. Energy Res.

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In the context of Integrated Energy System (IES), accurate short-term power demand forecasting is crucial for ensuring system reliability, optimizing operational efficiency through resource allocation, and supporting effective real-time decision-making in energy management. However, achieving high forecasting accuracy faces significant challenges due to the inherent complexity and stochastic nature of IES’s short-term load profiles, resulting from diverse consumption patterns among end-users and the intricate coupling within the network of interconnected energy sources. To address this issue, a dedicated Short-Term Power Load Forecasting (STPLF) framework for IES is proposed, which relies on a newly developed hybrid deep learning architecture. The framework seamlessly combines Long Short-Term Memory (LSTM) with Temporal Convolutional Network (TCN), enhanced by an attention mechanism module. By merging these methodologies, the network leverages the parallel processing prowess of TCN alongside LSTM’s ability to retain long-range temporal information, thus enabling it to dynamically concentrate on relevant sections of time series data. This synergy leads to improved prediction accuracy and broader applicability. Furthermore, the integration of residual connections within the network structure serves to deepen its learning capabilities and enhance overall performance. Ultimately, results from a real case study of a user-level IES demonstrate that the Mean Absolute Percentage Error (MAPE) of the proposed framework on the test set is 2.35%. This error rate is lower than the averages of traditional methods (3.43%) and uncombined single submodules (2.80%).

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“…Several machine learning approaches are compared by the authors of [21] to forecast the load in Ontario, Canada two days into the future. Finally, the authors of [22] provide an overview of the methods for performing short-term forecasts.…”

Section: Short-term Forecastsmentioning

confidence: 99%

Attention-Focused Machine Learning Method to Provide the Stochastic Load Forecasts Needed by Electric Utilities for the Evolving Electrical Distribution System

O’Donnell

2023

Energies

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Greater variation in electrical load should be expected in the future due to the increasing penetration of electric vehicles, photovoltaics, storage, and other technologies. The adoption of these technologies will vary by area and time, and if not identified early and managed by electric utilities, these new customer needs could result in power quality, reliability, and protection issues. Furthermore, comprehensively studying the uncertainty and variation in the load on circuit elements over periods of several months has the potential to increase the efficient use of traditional resources, non-wires alternatives, and microgrids to better serve customers. To increase the understanding of electrical load, the authors propose a multistep, attention-focused, and efficient machine learning process to provide probabilistic forecasts of distribution transformer load for several months into the future. The method uses the solar irradiance, temperature, dew point, time of day, and other features to achieve up to an 86% coefficient of determination (R2).

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Short-Term Load Forecasting Models: A Review of Challenges, Progress, and the Road Ahead

Cited by 37 publications

References 122 publications

Optimal allocation of distributed generation in meshed power networks: A metaheuristic approach

Optimal allocation of distributed generation in meshed power networks: A metaheuristic approach

Short-term power load forecasting for integrated energy system based on a residual and attentive LSTM-TCN hybrid network

Attention-Focused Machine Learning Method to Provide the Stochastic Load Forecasts Needed by Electric Utilities for the Evolving Electrical Distribution System

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