Online Hour-Ahead Load Forecasting Using Appropriate Time-Delay Neural Network Based on Multiple Correlation–Multicollinearity Analysis in IoT Energy Network

Zamee, Muhammad Ahsan; Han, Dong; Won, Dong-Jun

doi:10.1109/jiot.2021.3133002

Cited by 12 publications

(4 citation statements)

References 44 publications

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“…Additionally, the proposed method of [51] consists of two main parts, i.e., data refinement, and training, respectively. Furthermore, another ANN-based load forecasting method has been introduced by [52]. In [52], the online training-based approach is deployed for hour-ahead load forecasting and the architecture of the ANN is based on the appropriate time-delay neural network.…”

Section: Deep Learning-based Load Forecastingmentioning

confidence: 99%

“…Furthermore, another ANN-based load forecasting method has been introduced by [52]. In [52], the online training-based approach is deployed for hour-ahead load forecasting and the architecture of the ANN is based on the appropriate time-delay neural network. As mentioned above, there are some examples to use different types of the ANNs in different ways for the prediction of the electrical loads.…”

Section: Deep Learning-based Load Forecastingmentioning

confidence: 99%

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Deep Learning for Forecasting-Based Applications in Cyber–Physical Microgrids: Recent Advances and Future Directions

et al. 2023

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Renewable energy resources can be deployed locally and efficiently using the concept of microgrids. Due to the natural uncertainty of the output power of renewable energy resources, the planning for a proper operation of microgrids can be a challenging task. In addition, the information about the loads and the power consumption of them can create benefits to increase the efficiency of the microgrids. However, electrical loads can have uncertainty due to reasons such as unpredictable behavior of the consumers. To exploit a microgrid, energy management is required at the upper level of operation and control in order to reduce the costs. One of the most important tasks of the energy management system is to satisfy the loads and, in other words, develop a plan to maintain equilibrium between the power generation and power consumption. To obtain information about the output power of renewable energy resources and power consumption, deep learning can be implemented as a powerful tool, which is able to predict the desired values. In addition, weather conditions can affect the output power of renewable energy-based resources and the behavior of the consumers and, as a result, the power consumption. So, deep learning can be deployed for the anticipation of the weather conditions. This paper will study the recent works related to deep learning, which has been implemented for the prediction of the output power of renewable energy resources (i.e., PVs and wind turbines), electrical loads, and weather conditions (i.e., solar irradiance and wind speed). In addition, for possible future directions some strategies are suggested, the most important of which is the implementation of quantum computing in cyber–physical microgrids.

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Section: Deep Learning-based Load Forecastingmentioning

confidence: 99%

Section: Deep Learning-based Load Forecastingmentioning

confidence: 99%

Deep Learning for Forecasting-Based Applications in Cyber–Physical Microgrids: Recent Advances and Future Directions

et al. 2023

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show abstract

“…However, such improvement is not endless, and, at d-7, the historical information is already sufficient due to the highly cyclical and periodic nature of electrical demand. A wider window width actually introduces redundant information that is irrelevant to forecasts, which actually forms the multicollinearity effect [38][39][40].…”

Section: Window Sliding Width Of Load Variablementioning

confidence: 99%

Extreme Gradient Boosting Model for Day-Ahead STLF in National Level Power System: Estonia Case Study

Zhao,

Liu,

Fang

2023

Energies

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Short-term power load forecasting refers to the use of load and weather information to forecast the Day-ahead load, which is very important for power dispatch and the establishment of the power spot market. In this manuscript, a comprehensive study on the frame of input data for electricity load forecasting is proposed based on the extreme gradient boosting algorithm. Periodicity was the first of the historical load data to be analyzed using discrete Fourier transform, autocorrelation function, and partial autocorrelation function to determine the key width of a sliding window for an optimization load feature. The mean absolute error (MAE) of the frame reached 52.04 using a boosting model with a 7-day width in the validation dataset. Second, the fusing of datetime variables and meteorological information factors was discussed in detail and determined how to best improve performance. The datetime variables were determined as a form of integer, sine–cosine pairs, and Boolean-type combinations, and the meteorological features were determined as a combination with 540 features from 15 sampled sites, which further decreased MAE to 44.32 in the validation dataset. Last, a training method for day-ahead forecasting was proposed to combine the Minkowski distance to determine the historical span. Under this framework, the performance has been significantly improved without any tuning for the boosting algorithm. The proposed method further decreased MAE to 37.84. Finally, the effectiveness of the proposed method is evaluated using a 200-day load dataset from the Estonian grid. The achieved MAE of 41.69 outperforms other baseline models, with MAE ranging from 65.03 to 104.05. This represents a significant improvement of 35.89% over the method currently employed by the European Network of Transmission System Operators for Electricity (ENTSO-E). The robustness of the proposal method can be also guaranteed with excellent performance in extreme weather and on special days.

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“…Traditional load forecasting methods are usually based on statistics and experience, making it difficult to adapt to complex and ever-changing energy network environments. Zamee et al [20] studied a data-driven load forecasting method to improve prediction accuracy and stability, which is of great significance for ensuring the stable operation of energy networks and energy conservation and emission reduction. Multi correlation STDNN (MD STDNN) is a new neural network model that combines multi correlation technology with STDNN, and its application in load forecasting has also achieved good results.…”

Section: Based Feature Extraction and Processing Of Music Datamentioning

confidence: 99%

Digital Art Creation and Optimization of Intangible Cultural Heritage Based on Image Processing Algorithm

2023

CADandA

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In music information retrieval (MIR), deep learning (DL) can use its powerful feature extraction ability to extract more effective features from music data. The traditional MIR method can't effectively deal with the diversity, dynamics, heterogeneity and real-time characteristics of Internet of Things (IoT) data. This article proposes a DL-based MIR and IoT recommendation system, aiming at providing more accurate and efficient computer-aided MIR and recommendation services. In order to evaluate the performance of IoT music recommendation system based on different recommendation algorithms, the advantages and disadvantages of various recommendation algorithms are analyzed through comparative experiments and system load test experiments. The results show that DL algorithm has obvious advantages in all indicators, followed by Collaborative Filtering (CF) algorithm and matrix decomposition-based algorithm, while the recommendation effect of Knearest neighbor algorithm is relatively poor. The implementation of this algorithm can provide useful reference and support for the design and optimization of IoT recommendation system, improve the performance and stability of recommendation system, and thus promote the sustainable growth of music industry.

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Online Hour-Ahead Load Forecasting Using Appropriate Time-Delay Neural Network Based on Multiple Correlation–Multicollinearity Analysis in IoT Energy Network

Cited by 12 publications

References 44 publications

Deep Learning for Forecasting-Based Applications in Cyber–Physical Microgrids: Recent Advances and Future Directions

Deep Learning for Forecasting-Based Applications in Cyber–Physical Microgrids: Recent Advances and Future Directions

Extreme Gradient Boosting Model for Day-Ahead STLF in National Level Power System: Estonia Case Study

Digital Art Creation and Optimization of Intangible Cultural Heritage Based on Image Processing Algorithm

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