Short-term reforecasting of power output from a 48 MWe solar PV plant

Chu, Yi; Urquhart, B.; Gohari, Seyyed M.I.; Pedro, Hugo T.C.; Kleissl, Jan; Coimbra, Carlos F.M.

doi:10.1016/j.solener.2014.11.017

Cited by 228 publications

(67 citation statements)

References 40 publications

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“…Cloud locations are predicted for 5 minutes ahead and simulations showed that for coastal areas, cloud forecast error increases as the forecast horizon increases. A neural network-based reforecasting method is developed in [8] to improve forecast accuracy of (1) a physical model based on cloud tracking, (2) auto regressive moving average and (3) k-th nearest neighbor methods for forecast horizons of 15 minutes and less. A recent review of the state-of-the-art can be found in [9].…”

Section: Introductionmentioning

confidence: 99%

Very Short-Term Nonparametric Probabilistic Forecasting of Renewable Energy Generation— With Application to Solar Energy

Golestaneh

Pinson

Gooi

2016

IEEE Trans. Power Syst.

237

107

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Abstract-Due to the inherent uncertainty involved in renewable energy forecasting, uncertainty quantification is a key input to maintain acceptable levels of reliability and profitability in power system operation. A proposal is formulated and evaluated here for the case of solar power generation, when only power and meteorological measurements are available, without skyimaging and information about cloud passages. Our empirical investigation reveals that the distribution of forecast errors do not follow any of the common parametric densities. This therefore motivates the proposal of a nonparametric approach to generate very short-term predictive densities, i.e., for lead times between a few minutes to one hour ahead, with fast frequency updates. We rely on an Extreme Learning Machine (ELM) as a fast regression model, trained in varied ways to obtain both point and quantile forecasts of solar power generation. Four probabilistic methods are implemented as benchmarks. Rival approaches are evaluated based on a number of test cases for two solar power generation sites in different climatic regions, allowing us to show that our approach results in generation of skilful and reliable probabilistic forecasts in a computationally efficient manner.

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Section: Introductionmentioning

confidence: 99%

Very Short-Term Nonparametric Probabilistic Forecasting of Renewable Energy Generation— With Application to Solar Energy

Golestaneh

Pinson

Gooi

2016

IEEE Trans. Power Syst.

237

107

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“…To account for the angle between the direction of sunlight The output power of PVs is highly related to intensity of illumination. Thus, the output power of PVs can be calculated if intensity of illumination is known [27]. Assume that wind speed conforms to a Weibull distribution.…”

Section: Study Casementioning

confidence: 99%

Optimized Daily Dispatching Strategy of Building- Integrated Energy Systems Considering Vehicle to Grid Technology and Room Temperature Control

Wang

Tang²,

Chen

et al. 2018

Energies

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Abstract:With the development of vehicle-to-grid (V2G) technology, electric vehicles (EVs) are not only a promising transportation solution, but also can be used as mobile energy storage and spinning reserves, which play an important role in building-integrated energy systems (BIESs) and can further promote energy efficiency. Considering the space-time characteristics of EVs and the room temperature demand, this study establishes a planning model using V2G technology to minimize daily dispatch cost. Four kinds of control modes were proposed, combining the technology of the resident-owned and staff-owned EVs, in combination with the internal heating and power supply system. In this paper, the operating state of the system and the interaction of the equipment are analyzed under different charging and discharging control modes of EVs. The economics of the microgrid of the comprehensive energy building under four control modes are also discussed. Simulation results indicate that the combined control mode of residential vehicle and office vehicle is optimal for building an integrated energy microgrid, and the room temperature requirements can also be used as an important income source for building the microgrid.

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“…Forecast can thus be obtained through persisting the motion vectors or more sophistically, by solving the advection-diffusion equation [49]. In recent reports it has been found that forecasting based on deterministic ray tracing method produces forecasts that are worse than persistence, at 5, 10, 15 min forecast horizon [50]. In terms of normalized Root Mean Square Error (nRMSE), forecast error using TSI varies from 18 to 24% for forecast horizons ranging from 30 s to 15 min [45].…”

Section: Total Sky Imagersmentioning

confidence: 99%

Solar Power Forecasting: A Review

K.¹,

Isha²

2016

IJCA

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The increasing demand for energy is one of the biggest reasons behind the integration of solar energy into the electric grids or networks. To ensure the efficient use of energy PV systems it becomes important to forecast information reliably. The accurate prediction of solar irradiance variation can enhance the quality of service. This integration of solar energy and accurate prediction can help in better planning and distribution of energy.Here in this paper, a deep review of methods which are used for solar irradiance forecasting is presented. These methods help in selecting the appropriate forecast technique according to the needs or requirements. This paper also presents the metrics that are used for evaluating the performance of a forecast model.

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Short-term reforecasting of power output from a 48 MWe solar PV plant

Cited by 228 publications

References 40 publications

Very Short-Term Nonparametric Probabilistic Forecasting of Renewable Energy Generation— With Application to Solar Energy

Very Short-Term Nonparametric Probabilistic Forecasting of Renewable Energy Generation— With Application to Solar Energy

Optimized Daily Dispatching Strategy of Building- Integrated Energy Systems Considering Vehicle to Grid Technology and Room Temperature Control

Solar Power Forecasting: A Review

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