Development of algorithm for day ahead PV generation forecasting using data mining method

Kang, Mincheol; Sohn, Jin-Man; Park, Jong-young; Lee, Song-Keun; Yoon, Yong-Tae

doi:10.1109/mwscas.2011.6026333

Cited by 26 publications

(12 citation statements)

References 2 publications

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“…Sobu and Wu (2012), Kang et al (2011), Mori and Takahashi (2012). The main types of time series models include-parametric and non-parametric time series models.…”

Section: Time Series Models For Predictionmentioning

confidence: 99%

Time series decomposition model for accurate wind speed forecast

Prema¹,

Rao²

2015

Renewables

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Climate change can be considered to be the greatest environmental challenge our world is facing today. Along with the need to ensure long-term assurance of energy supply, it imposes an obligation on all of us to consider ways of reducing our carbon footprint and sourcing more of our energy from renewable sources. Wind energy is one such source and forecasting methods for the prediction of wind speed are becoming increasingly significant due to the penetration of wind power as an alternative to conventional energy sources. This paper proposes time series models for short-term prediction of wind speed. The predictions are done for 1 day ahead using different time series models. For each model, these predicted values are compared with the actual values for the next day. Basic exponential smoothing for different duration of data was tested. A hybrid model with decomposition and exponential smoothing is proposed. A multiplicative decomposition is carried out for the measured data. Separate models were developed for seasonal and trend series and then combined to carry out the forecast. The models were tested for different durations of samples and different weather conditions. It is observed from the results that the prediction with decomposition model for 4 months data gave the least error.

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“…Sobu and Wu (2012), Kang et al (2011), Mori and Takahashi (2012). The main types of time series models include-parametric and non-parametric time series models.…”

Section: Time Series Models For Predictionmentioning

confidence: 99%

Time series decomposition model for accurate wind speed forecast

Prema¹,

Rao²

2015

Renewables

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“…In several research works it was discovered, that a forecasting of power profiles for a PV-system at a certain location based on weather forecasts is rather inaccurate [7], [8].…”

Section: B Model For the Energy Offer From A Pv-systemmentioning

confidence: 99%

Eco-friendly application of EVs for home-to-work and home-to-education transports

Dziadek

Feucht

Mittnacht

et al. 2013

2013 IEEE International Conference on Industrial Technology (ICIT)

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Electric vehicles (EVs) today in general are operated with energy from the electric grid. So energy for the EVs is basically available at almost each place on the world. If the energy for the grids however is produced to a big extent from fossil fuels the EVs are not much more eco-friendly than conventional cars. In this paper a field of application is investigated where EVs can be operated very eco-friendly by renewable energy. EVs which are used mainly for home-to-work and home-to-education transports can be charged at the placesof-work respectively at the places of education directly from PVsystems. In a field test with six electric scooters, which were used by students for their travels from their homes to our university, the characteristics of these EVs at a charging station at our university was analyzed. Based on these results the charging of the EVs during typical days was analyzed. The results are showing that the EVs can be charged to a big extent from a very limited PV-system.

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“…An accurate prediction of generation for about 10 h would be ideal for its optimal performance. In such cases, a range of errors around 10% or less is to be expected [22,23]. In addition, during evening and night h (6 PM-9 AM), the forecast can be even more accurate due to minimal solar radiation and thus, a small PV production.…”

Section: Generation Forecastmentioning

confidence: 99%

Minimization of Losses in Smart Grids Using Coordinated Voltage Control

2012

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This article deals with the influence of distributed generation (DG) on distribution line losses with respect to voltage profile. The article focuses on the development of a control strategy to minimize the grid losses and assure fairness regarding reactive power contributions. As retail customers typically have no choice where they are located along a feeder, it seems unfair that only some of them bear all the burden and responsibility for the voltage rise. On the basis of new technologies, which are capable of fast communication and data processing, a new control system has been proposed that combines classical centralized and local control. The heart of the control system is a load-flow algorithm, which estimates the voltage drop using a modeled network. Different control solutions were evaluated by means of computer simulations. The simulated network is an actual Slovenian medium-voltage distribution network which covers a large area with diverse feeders and thus gives relatively general results.

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Development of algorithm for day ahead PV generation forecasting using data mining method

Cited by 26 publications

References 2 publications

Time series decomposition model for accurate wind speed forecast

Time series decomposition model for accurate wind speed forecast

Eco-friendly application of EVs for home-to-work and home-to-education transports

Minimization of Losses in Smart Grids Using Coordinated Voltage Control

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