An intelligent real-time power management system with active learning prediction engine for PV grid-tied systems

Kow, Ken Weng; Wong, Yee Wan; Rajkumar, R.; Isa, Dino

doi:10.1016/j.jclepro.2018.09.084

Cited by 14 publications

(7 citation statements)

References 32 publications

Supporting

Mentioning

Contrasting

Order By: Relevance

“…The author also pointed that the ARIMA model exhibited large errors at intermittent intervals, corresponding to the fast cloud transients that deeply impact PV reliability. These intermittent large errors are the events successfully predicted in the work by Kow et al [20].…”

Section: Short-term Forecastingsupporting

confidence: 73%

“…As explained before, these models aim to predict the future state of a certain aspect of solar variability. The approaches using cloud tracking in sky images, as proposed by Chow et al [37] and Kow et al [20], add components of physical and geometrical modeling of cloud systems. Since the main actor in short-term variability is related to passing clouds, relevant information on their dynamic provides a more comprehensive characterization [38].…”

Section: Short-term Forecastingmentioning

confidence: 99%

“…This tendency points towards the superiority of using sky-images over what Diagne et al [12] refer to as statistical models. In the study conducted by Kow et al [20] it becomes apparent just how powerful sky-image based forecasting can be, achieving a detection rate of over 90% of power fluctuation events and mitigation of almost 80% of power fluctuation events with minimal energy loss.…”

Section: Introductionmentioning

confidence: 99%

“…Kow et al (2018) [20] Forecast of PV power 30 s ahead coupled with mitigation system GHI every 1 s + ambient temperature every 1 s and PV system modeled power; Self-organizing incremental neural network (M-SOINN) with active learning for forecasting power; Non-supervised method capable of forecasting power output of PV system 30 s ahead.Kuhn et al (2018) [64]Forecast of 1-min-average GHI from 0 to 15 min ahead Cloud segmentation, detection and georeferencing, using 4 sky cameras (WobaS-4cam) and 4-dimensional CSL; Irradiance maps validated with ground irradiance sensors and shadow camera; GHI and DNI obtained from geo-located shadow map and radiometer measurements at previous time steps.…”

mentioning

confidence: 99%

See 3 more Smart Citations

Development of a Low-Cost Data Acquisition System for Very Short-Term Photovoltaic Power Forecasting

2021

View full text Add to dashboard Cite

The rising adoption of renewable energy sources means we must turn our eyes to limitations in traditional energy systems. Intermittency, if left unaddressed, may lead to several power-quality and energy-efficiency issues. The objective of this work is to develop a working tool to support photovoltaic energy forecast models for real-time operation applications. The current paradigm of intra-hour solar-power forecasting is to use image-based approaches to predict the state of cloud composition for short time horizons. Since the objective of intra-minute forecasting is to address high-frequency intermittency, data must provide information on and surrounding these events. For that purpose, acquisition by exception was chosen as the guiding principle. The system performs power measurements at 1 Hz frequency, and whenever it detects variations over a certain threshold, it saves the data 10 s before and 4 s after the detection point. A multilayer perceptron neural network was used to determine its relevance to the forecasting problem. With a thorough selection of attributes and network structures, the results show very low error with R2 greater than 0.93 for both input variables tested with a time horizon of 60 s. In conclusion, the data provided by the acquisition system yielded relevant information for forecasts up to 60 s ahead.

show abstract

Section: Short-term Forecastingsupporting

confidence: 73%

Section: Short-term Forecastingmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

mentioning

confidence: 99%

See 2 more Smart Citations

Development of a Low-Cost Data Acquisition System for Very Short-Term Photovoltaic Power Forecasting

2021

View full text Add to dashboard Cite

show abstract

“…Moreover, AI was used for a similar application in [34], where the deep learning and wavelet transform integrated approach was used for short-term solar PV power prediction. The authors of [35] developed an intelligent real-time power management system, where an incremental unsupervised neural network algorithm was used to predict the output power and then detect the power fluctuations occurrence of a grid-tied PV system. A comparative study on short-term PV power prediction using the decomposition based ELM algorithm was presented in [36],…”

Section: Introductionmentioning

confidence: 99%

Review of Artificial Intelligence-Based Failure Detection and Diagnosis Methods for Solar Photovoltaic Systems

2021

View full text Add to dashboard Cite

In recent years, the overwhelming growth of solar photovoltaics (PV) energy generation as an alternative to conventional fossil fuel generation has encouraged the search for efficient and more reliable operation and maintenance practices, since PV systems require constant maintenance for consistent generation efficiency. One option, explored recently, is artificial intelligence (AI) to replace conventional maintenance strategies. The growing importance of AI in various real-life applications, especially in solar PV applications, cannot be over-emphasized. This study presents an extensive review of AI-based methods for fault detection and diagnosis in PV systems. It explores various fault types that are common in PV systems and various AI-based fault detection and diagnosis techniques proposed in the literature. Of note, there are currently fewer literatures in this area of PV application as compared to the other areas. This is due to the fact that the topic has just recently been explored, as evident in the oldest paper we could obtain, which dates back to only about 15 years. Furthermore, the study outlines the role of AI in PV operation and maintenance, and the main contributions of the reviewed literatures.

show abstract

Information-Infrastructure Mechanism for Managing Industrial Enterprise Self-Development in the Setting of Digitization

Бабкин

Мыльникова

Chernovalova

et al. 2021

XIV International Scientific Conference “INTERAGROMASH 2021"

View full text Add to dashboard Cite

An intelligent real-time power management system with active learning prediction engine for PV grid-tied systems

Cited by 14 publications

References 32 publications

Development of a Low-Cost Data Acquisition System for Very Short-Term Photovoltaic Power Forecasting

Development of a Low-Cost Data Acquisition System for Very Short-Term Photovoltaic Power Forecasting

Review of Artificial Intelligence-Based Failure Detection and Diagnosis Methods for Solar Photovoltaic Systems

Information-Infrastructure Mechanism for Managing Industrial Enterprise Self-Development in the Setting of Digitization

Contact Info

Product

Resources

About