Interval prediction method based on Long-Short Term Memory networks for system integrated of hydro, wind and solar power

Zhang, Zhendong; Hui, Qing; Yao, Liqiang; Lu, Jiming; Cheng, Liangge

doi:10.1016/j.egypro.2019.01.491

Cited by 18 publications

(3 citation statements)

References 12 publications

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“…A negative R-square value can be created by a variety of circumstances [12]. The complexity of user review data is one possible factor.…”

Section: Resultsmentioning

confidence: 99%

Sentiment Analysis on the Quality of Public Services with User Satisfaction Prediction of YuhSinau Application Managed by BKPSDM Kabupaten Kebumen Using LSTM Method

Rizki Amelia,

Rizal Isnanto

2024

KSS

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The quality of public services is critical in providing effective and responsive governance in an increasingly digital society. The development of the YuhSinau application by the Personnel and Human Resource Development Agency (BKPSDM) of Kebumen Regency offers an innovative response to the growing need for e-learning solutions for local government civil servants (Pegawai Negeri Sipil or PNS). However, determining the app’s effectiveness and user satisfaction is critical. This demands a thorough sentiment analysis in order to acquire insights into users’ thoughts and opinions about the quality of public services supplied by YuhSinau. The Long Short-Term Memory (LSTM) approach is used in this article to examine feelings and forecast customer pleasure. Data collection from multiple sources, initial data preprocessing, LSTM model construction, training, validation, and prediction are all part of the process. The results show that the model has some drawbacks, most notably its failure to appropriately explain variation in target data due to a negative R-squared value. Enhancements to the LSTM architecture, hyperparameter adjustment, and the use of more diverse and representative training data are proposed to improve the model. Continuous review and responsiveness to user comments are critical for improving the quality of public services via the YuhSinau application. Keywords: sentiment analysis, public service quality, user satisfaction, YuhSinau application,LSTM method

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“…A negative R-square value can be created by a variety of circumstances [12]. The complexity of user review data is one possible factor.…”

Section: Resultsmentioning

confidence: 99%

Sentiment Analysis on the Quality of Public Services with User Satisfaction Prediction of YuhSinau Application Managed by BKPSDM Kabupaten Kebumen Using LSTM Method

Rizki Amelia,

Rizal Isnanto

2024

KSS

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“…Another advantage of LSTM cell compared to a typical intermittent unit is its cell memory unit. One of the limitations of LSTM is that there is no memory associated with the model which causes problems for sequential data, like text or time series [89], [90].…”

Section: ) Long Short-term Memory-based Hybrid Approachmentioning

confidence: 99%

Artificial Intelligence Based Hybrid Forecasting Approaches for Wind Power Generation: Progress, Challenges and Prospects

et al. 2021

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Globally, wind energy is growing rapidly and has received huge consideration to fulfill global energy requirements. An accurate wind power forecasting is crucial to achieve a stable and reliable operation of the power grid. However, the unpredictability and stochastic characteristics of wind power affect the grid planning and operation adversely. To address these concerns, a substantial amount of research has been carried out to introduce an efficient wind power forecasting approach. Artificial Intelligence (AI) approaches have demonstrated high precision, better generalization performance and improved learning capability, thus can be ideal to handle unstable, inflexible and intermittent wind power. Recently, AI-based hybrid approaches have become popular due to their high precision, strong adaptability and improved performance. Thus, the goal of this review paper is to present the recent progress of AI-enabled hybrid approaches for wind power forecasting emphasizing classification, structure, strength, weakness and performance analysis. Moreover, this review explores the various influential factors toward the implementations of AI-based hybrid wind power forecasting including data preprocessing, feature selection, hyperparameters adjustment, training algorithm, activation functions and evaluation process. Besides, various key issues, challenges and difficulties are discussed to identify the existing limitations and research gaps. Finally, the review delivers a few selective future proposals that would be valuable to the industrialists and researchers to develop an advanced AI-based hybrid approach for accurate wind power forecasting toward sustainable grid operation.INDEX TERMS Wind power forecasting, artificial intelligence, machine learning, deep learning, optimization, hybrid approaches.

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“…Besides the relatively large data requirements for UCED methods are also likely to pose challenges for medium-and long-term operation simulation. Furthermore, some researches are devoted to achieving the prediction of uncertainties in medium-and long-term [14], [15], and simulate operation condition based on the predicted results [16]. Apparently, by this method, operation simulation results are mainly depended on the precision of prediction, which will bring difficulties to appropriate and accurate decisionmaking.…”

mentioning

confidence: 99%

Research on Medium- and Long-Term Operation Simulation Method Based on Improved Universal Generating Function

Guo¹,

Zheng²,

Li³

2019

IEEE Access

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The development of technologies in generation-grid-load-energy storage has created enormous uncertainties for power system and thus brings great challenges to medium-and long-term operation simulation. In order to describe uncertainties in medium-and long-term operation process and analyze operation condition objectively, this paper firstly builds multi-state probabilistic models of generation-grid-load-energy storage based on multi-state analysis method, and adopt Markov method to consider state dependency of some components. And then using these models, an operation simulation method based on the improved universal generating function is proposed in which the minimum distance classification method is adopted to improve computational efficiency and avoid state explosion problem. At last the validation and engineering practicality of the proposed method are demonstrated in IEEE RTS-79 and Zhangjiakou power system respectively, and the suitable areas of different operation simulation method are also discussed. INDEX TERMS Medium-and long-term operation simulation, multi-state analysis, universal generating function, uncertainty analysis. NOMENCLATURE ABBREVIATIONS

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Interval prediction method based on Long-Short Term Memory networks for system integrated of hydro, wind and solar power

Cited by 18 publications

References 12 publications

Sentiment Analysis on the Quality of Public Services with User Satisfaction Prediction of YuhSinau Application Managed by BKPSDM Kabupaten Kebumen Using LSTM Method

Sentiment Analysis on the Quality of Public Services with User Satisfaction Prediction of YuhSinau Application Managed by BKPSDM Kabupaten Kebumen Using LSTM Method

Artificial Intelligence Based Hybrid Forecasting Approaches for Wind Power Generation: Progress, Challenges and Prospects

Research on Medium- and Long-Term Operation Simulation Method Based on Improved Universal Generating Function

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