A New Prediction Model Based on Cascade NN for Wind Power Prediction

Torabi, Amirhosein; Mousavy, Sayyed Ali Kiaian; Dashti, Vahideh; Saeedi, Mohammadhossein; Yousefi, Nasser

doi:10.1007/s10614-018-9795-8

Cited by 19 publications

(6 citation statements)

References 20 publications

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“…The energy trading market is leading (also in complexity) and can accurately forecast 36 h ahead at high frequencies using ensemble learning as described by Suárez-Cetrulo et al [38], which shows several techniques obtaining a scaled RMSE and scaled mean absolute error of less than 1 × 10 −3 . Another promising long-term prediction model is demonstrated by Torabi et al [39], a cascade neural network that is able to improve one-day-ahead forecasts by 84% and one-week-ahead predictions by 73% based on the RMSE of other already progressive prediction models. Skittides and Früh [40] propose a wind forecasting tool based on Principal Component Analysis (PCA), which is trained on past data to predict wind speeds using an ensemble of dynamically similar past events, and show good performance in forecasting the wind up to 24 h ahead.…”

Section: Related Workmentioning

confidence: 99%

Improving the Efficiency of Renewable Energy Assets by Optimizing the Matching of Supply and Demand Using a Smart Battery Scheduling Algorithm

et al. 2023

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Given the fundamental role of renewable energy assets in achieving global temperature control targets, new energy management methods are required to efficiently match intermittent renewable generation and demand. Based on analysing various designed cases, this paper explores a number of heuristics for a smart battery scheduling algorithm that efficiently matches available power supply and demand. The core of improvement of the proposed smart battery scheduling algorithm is exploiting future knowledge, which can be realized by current state-of-the-art forecasting techniques, to effectively store and trade energy. The performance of the developed heuristic battery scheduling algorithm using forecast data of demands, generation, and energy prices is compared to a heuristic baseline algorithm, where decisions are made solely on the current state of the battery, demand, and generation. The battery scheduling algorithms are tested using real data from two large-scale smart energy trials in the UK, in addition to various types and levels of simulated uncertainty in forecasts. The results show that when using a battery to store generated energy, on average, the newly proposed algorithm outperforms the baseline algorithm, obtaining up to 20–60% more profit for the prosumer from their energy assets, in cases where the battery is optimally sized and high-quality forecasts are available. Crucially, the proposed algorithm generates greater profit than the baseline method even with large uncertainty on the forecast, showing the robustness of the proposed solution. On average, only 2–12% of profit is lost on generation and demand uncertainty compared to perfect forecasts. Furthermore, the performance of the proposed algorithm increases as the uncertainty decreases, showing great promise for the algorithm as the quality of forecasting keeps improving.

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Section: Related Workmentioning

confidence: 99%

Improving the Efficiency of Renewable Energy Assets by Optimizing the Matching of Supply and Demand Using a Smart Battery Scheduling Algorithm

et al. 2023

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“…For this purpose, we use an artificial neural network (ANN). ANNs have been widely used as a very powerful method for time series prediction in different fields regarding the power grid [30,31,32]. Especially, for load and energy forecasts ANNs are preferred due to the nonlinearity and randomness within power data [8].…”

Section: Very Short Term Power Predictionmentioning

confidence: 99%

A Non-Intrusive Load Monitoring Approach for Very Short Term Power Predictions in Commercial Buildings

Brucke¹,

Arens²,

Telle³

et al. 2020

Preprint

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This paper presents a new algorithm to extract device profiles fully unsupervised from three phases reactive and active aggregate power measurements. The extracted device profiles are applied for the disaggregation of the aggregate power measurements using particle swarm optimization. Finally, this paper provides a new approach for short term power predictions using the disaggregation data. For this purpose, a state changes forecast for every device is carried out by an artificial neural network and converted into a power prediction afterwards by reconstructing the power regarding the state changes and the device profiles. The forecast horizon is 15 minutes. To demonstrate the developed approaches, three phase reactive and active aggregate power measurements of a multi-tenant commercial building are used. The granularity of data is 1 s. In this work, 52 device profiles are extracted from the aggregate power data. The disaggregation shows a very accurate reconstruction of the measured power with a percentage energy error of approximately 1 %. The developed indirect power prediction method applied to the measured power data outperforms two persistence forecasts and an artificial neural network, which is designed for 24h-day-ahead power predictions working in the power domain.

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“…However, due to time progression, such applications are now in more than business sections of economies. For example the latest applications of data mining can be found in Torabi et al [3] where they developed a new prediction model for energy production with wind turbines. On the same note, Mostafa [4] took a review of some techniques and applications of data mining concepts.…”

Section: Introductionmentioning

confidence: 99%

Fraud Detection and Fraudulent Risks Management in the Insurance Sector Using Selected Data Mining Tools

Mushunje¹

2019

AJDMKD

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Knowledge discovery, shortly known as Data mining plays a crucial role within the insurance sector. Serious troublesome cases such as fraudulent cases can be well managed in the insurance sector through data mining application. In this paper, we aim to put on surface the two forms of fraud that is softy and hard fraud, to give out the causes of such fraudulent acts and to state out different suggested data mining techniques that can be applied to the insurance data to detect fraud. Also, we aim to highlight other benefits that can be enjoyed from using data mining in the insurance sector. We conjectured and found that, application of data mining helps to quickly detect fraud, reduce operation cost and to improve profit margins and increased competitive advantages. We put forward that techniques such as association, clustering, classification and regression are good when detecting fraud from the insurance claims data and should be acquired and applied. We then recommended that, underwriters and insurance officials should contribute much in preventing fraudulent cases in the insurance sector. This is so because, prevention is better than cure. Above all, we concluded that, application of data mining techniques through sequential pattern mining can help much to predict any future and potential fraudulent cases. This is helpful on planning and to keep the insurers alert before the fraudulent risk occurs.

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A New Prediction Model Based on Cascade NN for Wind Power Prediction

Cited by 19 publications

References 20 publications

Improving the Efficiency of Renewable Energy Assets by Optimizing the Matching of Supply and Demand Using a Smart Battery Scheduling Algorithm

Improving the Efficiency of Renewable Energy Assets by Optimizing the Matching of Supply and Demand Using a Smart Battery Scheduling Algorithm

A Non-Intrusive Load Monitoring Approach for Very Short Term Power Predictions in Commercial Buildings

Fraud Detection and Fraudulent Risks Management in the Insurance Sector Using Selected Data Mining Tools

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