Short Term Load Forecasting Using Particle Swarm Optimization Based ANN Approach

Azzam-ul-Asar,; Hassnain, S. R.; Khan, Anwarzeb

doi:10.1109/ijcnn.2007.4371176

Cited by 13 publications

(5 citation statements)

References 10 publications

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“…Particle swarm optimization (PSO) is an EA method built upon the idea of the social interactions of humans. The research study in [38] introduces a neural network whose weights are adjusted through the PSO algorithm for faster training and better model convergence. A hybrid approach that integrates the PSO, WT and an adaptable fuzzy-based prediction network has been presented in [39] for predicting Portugal's short-term wind power.…”

Section: Related Workmentioning

confidence: 99%

Multi-Horizon Electricity Load and Price Forecasting Using an Interpretable Multi-Head Self-Attention and EEMD-Based Framework

Azam

Younis

2021

IEEE Access

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Accurate system marginal price and load forecasts play a pivotal role in economic power dispatch, system reliability and planning. Price forecasting helps electricity buyers and sellers in an energy market to make effective decisions when preparing their bids and making bilateral contracts. Despite considerable research work in this domain, load and price forecasting still remains to be a complicated task. Various uncertain elements contribute to electricity price and demand volatility, such as changes in weather conditions, outages of large power plants, fuel cost, complex bidding strategies and transmission congestion in the power system. Thus, to deal with such difficulties, we propose a novel hybrid deep learning method based upon bidirectional long short-term memory (BiLSTM) and a multi-head self-attention mechanisms that can accurately forecast locational marginal price (LMP) and system load on a day-ahead basis. Additionally, ensemble empirical mode decomposition (EEMD), a data-driven algorithm, is used for the extraction of hidden features from the load and price time series. Besides that, an intuitive understanding of how the proposed model works under the hood is demonstrated using different interpretability use cases. The performance of the presented method is compared with existing well-known techniques applied for short-term electricity load and price forecast in a comprehensive manner. The proposed method produces considerably accurate results in comparison to existing benchmarks.INDEX TERMS Deep learning, energy markets, ensemble empirical mode decomposition, multi-head self-attention, short-term load and price forecasting, kalman smoothing, model interpretability.

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Section: Related Workmentioning

confidence: 99%

Multi-Horizon Electricity Load and Price Forecasting Using an Interpretable Multi-Head Self-Attention and EEMD-Based Framework

Azam

Younis

2021

IEEE Access

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“…The future operation and management of power systems demand quicker decisionmaking and adaptability to unpredictability. There is a rising need for calibration and verification estimates in a variety of applications, including economic power production distribution, energy trading and system security assessments, optimal power exchange across grids, unit commitment, and performance monitoring (El-Hadad, Tan and Tan, 2022;Ul-Asar, Hassnain, and Khan, 2007).…”

Section: Introductionmentioning

confidence: 99%

Short-Term Load Forecasting Utilizing a Combination Model: A Brief Review

Ahmad,

Liu,

Hashim

et al. 2024

IJTech

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To deliver electricity to customers safely and economically, power companies encounter numerous economic and technical challenges in their operations. Power flow analysis, planning, and control of power systems stand out among these issues. Over the last several years, one of the most developing study topics in this vital and demanding discipline has been electricity short-term load forecasting (STLF). Power system dispatching, emergency analysis, power flow analysis, planning, and maintenance all require it. This study emphasizes new research on long short-term memory (LSTM) algorithms related to particle swarm optimization (PSO) inside this area of short-term load forecasting. The paper presents an in-depth overview of hybrid networks that combine LSTM and PSO and have been effectively used for STLF. In the future, the integration of LSTM and PSO in the development of comprehensive prediction methods and techniques for multi-heterogeneous models is expected to offer significant opportunities. With an increased dataset, the utilization of advanced multi-models for comprehensive power load prediction is anticipated to achieve higher accuracy.

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“…These models utilize various unique techniques to extract nonlinear features and multivariate time features. Notable examples include support vector regression (SVR) [13][14][15], Kalman filter (KF) [16,17], random forest regression (RF) [18,19], fuzzy logic framework (FL) [20,21], artificial neural network (ANN) [22,23], etc. The performances of machine learning models heavily depend on the manual selection of feature engineering, and a significant portion of studies in machine learning algorithms are focused on data preprocessing.…”

Section: Introductionmentioning

confidence: 99%

A New Hybrid Model Based on SCINet and LSTM for Short-Term Power Load Forecasting

Liu,

Li,

et al. 2023

Energies

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A stable and reliable power system is crucial for human daily lives and economic stability. Power load forecasting is the foundation of dynamically balancing between the power supply and demand sides. However, with the popularity of renewable energy sources and electric vehicles, it still struggles to achieve accurate power load forecasting due to the complex patterns and dynamics of load data. To mitigate these issues, this paper proposes a new hybrid model based on a sample convolution and integration network (SCINet) and a long short-term memory network (LSTM) for short-term power load forecasting. Specifically, a feed-forward network (FFN) is first used to enhance the nonlinear representation of the load data to highlight the complex temporal dynamics. The SCINet is then employed to iteratively extract and exchange information about load data at multiple temporal resolutions, capturing the long-term dependencies hidden in the deeper layers. Finally, the LSTM networks are performed to further strengthen the extraction of temporal dependencies. The principal contributions of the proposed model can be summarized as follows: (1) The SCINet with binary tree structure effectively extracts both local and global features, proving advantageous for capturing complex temporal patterns and dynamics; (2) Integrating LSTM into the SCINet-based framework mitigates information loss resulting from interactive downsampling, thereby enhancing the extraction of temporal dependencies; and (3) FNN layers are strategically designed to enhance the nonlinear representations prior to feeding the load data fed into the SCINet and LSTM. Three real-world datasets are used to validate the effectiveness and generalization of the proposed model. Experimental results show that the proposed model has superior performance in terms of evaluation metrics compared with other baseline models.

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Short Term Load Forecasting Using Particle Swarm Optimization Based ANN Approach

Cited by 13 publications

References 10 publications

Multi-Horizon Electricity Load and Price Forecasting Using an Interpretable Multi-Head Self-Attention and EEMD-Based Framework

Multi-Horizon Electricity Load and Price Forecasting Using an Interpretable Multi-Head Self-Attention and EEMD-Based Framework

Short-Term Load Forecasting Utilizing a Combination Model: A Brief Review

A New Hybrid Model Based on SCINet and LSTM for Short-Term Power Load Forecasting

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