Short Term Load Forecasting for Electrical Dispatcher of Baghdad City Based on SVM-PSO Method

Jaber, Aqeel S.; Satar, Koay A.; Shalash, Nadheer A.

doi:10.1109/icon-eei.2018.8784316

Cited by 14 publications

(5 citation statements)

References 10 publications

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“…In recent years, load forecasting methods represented by in-depth learning have been widely used because of their strong ability to process non-linear data and high prediction accuracy. More research has been done on load prediction of power system, mainly using fuzzy theory [5], support vector machine [6][7], grey model [8], random forest [9], autoregressive differential sliding average model [10], neural network [11][12][13], and integrated learning [14][15] to predict power load. These studies have achieved good predictions because they all have rich historical data and future load consumption patterns follow a historical cycle.…”

Section: Load Forecastingmentioning

confidence: 99%

Day-ahead load forecasting of integrated energy system based on transfer learning and ensemble learning

Liu¹,

Wang²,

Zhang³

et al. 2023

International Conference on Computer, Artificial Intelligence, and Control Engineering (CAICE 2023)

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As a new and efficient intelligent energy system, integrated energy system has been widely used. As more and more new buildings are incorporated into the system, accurate load forecasting is essential for the planning and operation of integrated energy systems. The historical data of new buildings incorporated into the energy management system is not enough to build accurate prediction models. Transfer learning, as a cross-domain learning method, has been applied in time series prediction. To solve the problem of negative transfer caused by fluctuation and randomness of load data, this paper presents a day-ahead power load forecasting model that combines transfer learning with ensemble learning. Firstly, a multivariate migration method based on data decomposition is proposed, which migrates the data of multiple buildings with high load similarity to enrich the historical data of the tar-get buildings and avoid negative transfer. Secondly, similar day and neural network integrated prediction models are presented to deal with the impact of different date types on prediction accuracy. Finally, the proposed model is validated by simulation experiments. The experimental results show that the proposed method achieves good prediction accuracy.

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Section: Load Forecastingmentioning

confidence: 99%

Day-ahead load forecasting of integrated energy system based on transfer learning and ensemble learning

Liu¹,

Wang²,

Zhang³

et al. 2023

International Conference on Computer, Artificial Intelligence, and Control Engineering (CAICE 2023)

View full text Add to dashboard Cite

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“…On the other hand, Intelligence optimization has revolutionized power engineering technology [24][25][26][27][28][29]. The new power quality has shaped the way of planning and control the power system.…”

Section: Introductionmentioning

confidence: 99%

Acceleration Factor Optimization of Gauss-Seidel Numerical Analysis for Power Flow Using FA

Yahyia,

Abbas,

Jaber

2023

J. Phys.: Conf. Ser.

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Load flow analysis is one of the essential ways used by electrical power engineers to achieve high system quality owing to the relevance of load flow in economic optimization, planning, and stability. This issue can be formulated as a set of nonlinear algebraic equations, which can be solved by iterative algorithms. Gauss-Seidel is one of the simple ways of determining load flow in a power system, with the acceleration factor being the most considerable modification when aiming at improving the accuracy of this method. This article reports the determination of the optimal acceleration factor using PSO and Firefly (FA) algorithms. The simulation was done in MATLAB using a 4-bus, 6-bus, and 9-bus of IEEE standard system for the determination of the optimal acceleration factor. The proposed methods were evaluated and benchmarked against the conventional value of the factor using the number of iterations as the fitness function. The outcome of the study showed the FA method as the most preferable method of identifying the optimum acceleration factor.

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“…The adoption of accurate prediction methods can enhance the stability and reliability of integrated energy systems (IESs). In recent years, load prediction methods, particularly those based on deep learning, have gained widespread use due to their ability to handle nonlinear data and achieve high prediction accuracy [3]. Over the years, numerous studies have investigated different methods for electrical load forecasting.…”

Section: Introductionmentioning

confidence: 99%

Classification of Anomaly Patterns in Integrated Energy Systems Based on Conditional Variational Autoencoder and Attention Mechanism

et al. 2023

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By studying the classification of anomaly patterns in integrated energy systems, a deeper understanding of their operational status can be gained, leading to improved reliability and efficiency. This can ultimately result in reduced energy consumption and carbon emissions, contributing to sustainability efforts. This paper proposes a classification method that employs a conditional variational autoencoder and attention mechanism for deep clustering to identify anomaly patterns and distinguish between normal and anomaly datasets. The proposed model effectively addresses the issue of detecting anomalous periods in traditional anomaly detection methods. To account for the periodicity and coupling relationships of different loads, the model integrates sliding windows and attention mechanisms to improve the accuracy of detecting anomaly patterns. Firstly, during the encoder stage, a spatial attention mechanism is incorporated to extract features at each time step of the model input. Secondly, during the decoder stage, a temporal attention mechanism is introduced to perform feature extraction among the multiple time-step hidden layer states of the model input. The proposed method is applied to a typical integrated energy system and compared with existing methods. The experimental results demonstrate the effectiveness of the proposed method in accurately classifying the normal and anomaly patterns of integrated energy systems due to the internal clustering evaluation index.

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Short Term Load Forecasting for Electrical Dispatcher of Baghdad City Based on SVM-PSO Method

Cited by 14 publications

References 10 publications

Day-ahead load forecasting of integrated energy system based on transfer learning and ensemble learning

Day-ahead load forecasting of integrated energy system based on transfer learning and ensemble learning

Acceleration Factor Optimization of Gauss-Seidel Numerical Analysis for Power Flow Using FA

Classification of Anomaly Patterns in Integrated Energy Systems Based on Conditional Variational Autoencoder and Attention Mechanism

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