Power System Load Forecasting Method Based on LSTM Network

Aiming at the problem of insufficient adaptability to the new elements of the new power system in the current distribution network investment method, this paper innovatively proposes a distribution network investment method based on the new power system. By constructing a source-grid-load-storage-side investment calculation model, the investment in the new power system can be accurately calculated. First, the distributed power investment is calculated from the two aspects of new construction and renovation. Secondly, construct the grid investment demand and grid investment capacity measurement model, and obtain the grid side investment model by weighted summation. Then, a model for calculating the scale of investment that can be saved due to demand-side response is constructed, and the cost of demand response is subtracted to obtain a model for calculating the scale of investment that can be saved on the load side. Finally, the energy storage side investment calculation model is constructed from the power supply side, grid side, user-side energy storage investment, and energy storage investment benefit. The research results are applied to the empirical area, and scientific guidance is provided to realize the precise investment in the area.

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“…Reference (Luo et al, 2021) builds a model by the cumulative generation of the grey generation method, then the model is:…”

Section: ) Forecast Of the Development Trend Of Influencing Factors B...mentioning

confidence: 99%

A novel investment strategy for renewable-dominated power distribution networks

Zhao¹,

Longjiang

Jing-liang³

et al. 2023

Front. Energy Res.

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“…LSTM is an advanced type of Recurrent Neural Network (RNN). Power system load forecasting has been based on this method [34], for quick detection in power system LSTM is used [35], and so many other works [36], [37], [38] has shown the popularity of LSTM has increased in the field of power system because of having to multitask learning ability. To enable the storage and access of information over a long period, a memory cell is imported into the RNN structure and it runs straight down the entire chain.…”

Section: ) Deep Learning-based Lstm Networkmentioning

confidence: 99%

Deep Learning Based Relay for Online Fault Detection, Classification, and Fault Location in a Grid-Connected Microgrid

et al. 2023

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In this article, a maiden attempt have been taken for the online detection of faults, classification of faults, and identification of the fault locations of a grid-connected Micro-grid (MG) system. A deep learning algorithm-based Long Short Term Memory (LSTM) network is proposed, for the first time, for the online detection of faults and their classifications of the considered MG system to overcome the issues that persist in the existing algorithms. Also, a combination of an LSTM network and feed-forward neural network (FFNN) with a back-propagation algorithm (BPA) is proposed to identify the exact locations of the faults since the identification of fault locations is more challenging than fault categorizations. To select a suitable deep learning network with multiple hidden layers for achieving the aforesaid objectives, a rigorous analysis has been done. To study the accuracy of the proposed techniques, different types of faults with different parameters are considered in this paper. An extensive simulation has been done in MATLAB/Simulink platform to study the performance of the system with the proposed techniques. To validate the effectiveness of the proposed techniques, the entire system is implemented in the real-time platform using the OPAL-RT digital simulator. Comparison has also been done for the results obtained using ANN and proposed techniques. The results show that the proposed techniques based on the deep learning network effectively detect, classify, and identify the location of different faults of an MG system with acceptable performances.

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Harnessing Deep Learning for Enhanced MPPT in Solar PV Systems: An LSTM Approach Using Real-World Data

Roy,

Adhikari,

Datta

et al. 2024

Electricity

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Maximum Power Point Tracking (MPPT) is essential for maximizing the efficiency of solar photovoltaic (PV) systems. While numerous MPPT methods exist, practical implementations often lean towards conventional techniques due to their simplicity. However, these traditional methods can struggle with rapid fluctuations in solar irradiance and temperature. This paper introduces a novel deep learning-based MPPT algorithm that leverages a Long Short-Term Memory (LSTM) deep neural network (DNN) to effectively track maximum power from solar PV panels, utilizing real-world data. The simulations of three algorithms—Perturb and Observe (P&O), Artificial Neural Network (ANN), and the proposed LSTM-based MPPT—were conducted using MATLAB (2021b) and RT_LAB (24.3.3) with an OPAL-RT simulator for real-time analysis. The data used for this study were sourced from NASA/POWER’s Native Resolution Daily Data of solar irradiation and temperature specific to Imphal, Manipur, India. The obtained results demonstrate that the LSTM-based MPPT system achieves a superior power tracking accuracy under changing solar conditions, producing an average output of 74 W. In comparison, the ANN and P&O methods yield average outputs of 57 W and 62 W, respectively. This significant improvement, i.e., 20–30%, underscores the effectiveness of the LSTM technique in enhancing the power output of solar PV systems. By incorporating real-world data, valuable insights into solar power generation specific to the selected location are provided. Furthermore, the outputs of the model were verified through real-time simulations using the OPAL-RT simulator OP4510, showcasing the practical applicability of this approach in real-world scenarios.

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Power System Load Forecasting Method Based on LSTM Network

Cited by 3 publications

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A novel investment strategy for renewable-dominated power distribution networks

A novel investment strategy for renewable-dominated power distribution networks

Deep Learning Based Relay for Online Fault Detection, Classification, and Fault Location in a Grid-Connected Microgrid

Harnessing Deep Learning for Enhanced MPPT in Solar PV Systems: An LSTM Approach Using Real-World Data

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