Sliding Window Regression based Short-Term Load Forecasting of a Multi-Area Power System

Khan, Irfan; Akber, Adnan; Xu, Yinliang

doi:10.1109/ccece.2019.8861915

Cited by 13 publications

(5 citation statements)

References 15 publications

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“…The RW can use some statistical techniques, such as machine learning techniques. RW gives impressive highly accurate results without prior experience [14]. The RW regression model is widely used because of its simple high-speed computations with highly accurate predictions.…”

Section: Previous Studiesmentioning

confidence: 99%

Adaptive Holt Winter Autoregressive Model for IoT Forecasting

Sawalha

Al‐Naymat

2023

Preprint

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In business and industry, forecasting the future values of a time series is an age-old and widely utilized data analysis strategy. IoT temporal data, used in various applications, is one of the most well-known examples of such a time series. The main problem is to forecast a future window of the data using the provided IoT temporal data. Many forecasting models that deal with such data were proposed, such as Rolling Window, SVR-RBF, ARIMA, etc. However, in the training process, these models use all available data or a previous direct window of data. As a result, the training data may contain irrelevant data patterns to the current situation, lowering the total forecasting accuracy. This paper proposes a new adaptive forecasting model called AHW-SWA by creating two models based on the Exponential Smoothing method. The first model is trained using the previous direct window of data. The second model uses an extracted data window from the far historical data with a pattern most similar to the current situation. Then an adaptive process is used to balance the predicted values from both models to achieve higher accuracy. AHW-SWA model reduces the overall forecasting error by comparing the extracted and the latest data patterns. Our model outperforms well-known models, including Rolling Window, SVR-RBF, and ARIMA.

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Section: Previous Studiesmentioning

confidence: 99%

Adaptive Holt Winter Autoregressive Model for IoT Forecasting

Sawalha

Al‐Naymat

2023

Preprint

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“…The setting of the adaptive sliding time window [29] should consider the width of the window and the fitting threshold. The window width represents the amount of intercepted data, and the fitting threshold represents the threshold value at which the fitting function can be accepted.…”

Section: Data Interception Based On Adaptive Sliding Time Windowmentioning

confidence: 99%

Research on New Energy Power System Stability Situation Awareness Based on Index Screening and Dynamic Evaluation

Meng

Wang

2023

Processes

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In order to integrate various operation information and accurately explore the operation situation of a power system, a dynamic comprehensive evaluation method considering index screening is proposed, and based on this, a situation awareness (SA) system based on the new energy power system is established. Firstly, aiming at the problem of information overlap among indicators, the existing indicator system of the new energy power system is quantitatively screened and reconstructed by the indicator screening method based on Measure of Sampling Adequacy (MSA) and Kaiser–Meyer–Olkin (KMO) test statistics. Afterwards, the dynamic evaluation method incorporating the subjective and objective is used to calculate the evaluation score and identify the stable operating state of the system. Finally, qualitative trend analysis and judgment are carried out on the evaluation scores, and the effect of power system situational awareness is verified by the improved IEEE 39 system. The results demonstrate that this method can be useful for improving the stability state sensing accuracy of a new energy power system.

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“…Many researchers have applied sliding window algorithms to various applications. In the field of power systems, some researchers utilised sliding window algorithms for power supply load forecasting [43], transient stability prediction [44], faulty equipment detection based on image recognition [45], and IED defect classification based on text mining [40]. In the field of anomaly detection, researchers applied sliding window algorithms to detect anomalies in different applications, such as IoT networks [9,10,46], and in-vehicle networks [47,48].…”

Section: F Sliding Window Algorithmsmentioning

confidence: 99%

Anomaly Detection for Insider Attacks From Untrusted Intelligent Electronic Devices in Substation Automation Systems

et al. 2022

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In recent decades, cyber security issues in IEC 61850-compliant substation automation systems (SASs) have become growing concerns. Many researchers have developed various strategies to detect malicious behaviours of SASs during the system operational stage, such as anomaly-based detection. However, most existing anomaly-based detection methods identify an abnormal behaviour by checking every single network packet without any association. These traditional methods cannot effectively detect "stealthy" attacks which modify legitimate messages slightly while imitating patterns of benign behaviours. In this paper, we present feature selection and extraction methods to generalise and summarise critical features when detecting insider attacks triggering from untrusted control devices within SASs. By applying a sliding window-based sequential classification mechanism, our detection method can detect anomalies across multiple devices without the need to learn datasets collected from all devices. Firstly, to generalise critical features and summarise systems' behaviours so that it is unnecessary to collect all datasets, we selected and extracted six critical network features from generic object-oriented substation events (GOOSE) messages and seven summarised physical features based on the general architecture of the primary plant of distribution substations. After that, to improve detection accuracy and reduce computational costs, we applied sliding window algorithms to divide datasets into different overlapped window-based snippets. Then we applied a sequential classification model based on Bidirectional Long Short-Term Memory networks to train and test those datasets. As a result, our method can detect insider attacks across multiple devices accurately with a false-negative rate of less than 1%.

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Sliding Window Regression based Short-Term Load Forecasting of a Multi-Area Power System

Cited by 13 publications

References 15 publications

Adaptive Holt Winter Autoregressive Model for IoT Forecasting

Adaptive Holt Winter Autoregressive Model for IoT Forecasting

Research on New Energy Power System Stability Situation Awareness Based on Index Screening and Dynamic Evaluation

Anomaly Detection for Insider Attacks From Untrusted Intelligent Electronic Devices in Substation Automation Systems

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