The “Weather Intelligence for Renewable Energies” Benchmarking Exercise on Short-Term Forecasting of  Wind and Solar Power Generation

Sperati, Simone; Alessandrini, Stefano; Pinson, Pierre; Kariniotakis, Georges

doi:10.3390/en8099594

Cited by 48 publications

(24 citation statements)

References 22 publications

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“…For comparative purposes, the results of the proposed model have been compared with the evaluations performed in [24], which refer to a benchmarking exercise organized within the framework of the European project "WIRE" [25] with the purpose of evaluating the performance of It is worth noting that the behavior of the analyzed 22nd of October helps us understand the reason why the values of the monthly modes are lower than those of the monthly means (Table 4). In fact, looking at Figure 7, it is possible to note that the majority of the days for all months are characterized by low error (resulting in a low value of the monthly mode) while only some days of all the months are characterized by very high error, resulting in an increased value of the monthly mean.…”

Section: Month Experimental Parametric Ss Mae (Pu) Ss Rmse (Pu) Ss Mamentioning

confidence: 99%

“…For comparative purposes, the results of the proposed model have been compared with the evaluations performed in [24], which refer to a benchmarking exercise organized within the framework of the European project "WIRE" [25] with the purpose of evaluating the performance of state-of-the-art models for short-term renewable energy forecasting. More specifically, 10 different solar power forecasting methods were applied to historical data for 2010 and 2011 and with reference to the suburbs of the city of Milan in Northern Italy and to the suburban area of Catania in Southern Italy.…”

Section: Month Experimental Parametric Ss Mae (Pu) Ss Rmse (Pu) Ss Mamentioning

confidence: 99%

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Solar Radiation Forecasting, Accounting for Daily Variability

2016

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Radiation forecast accounting for daily and instantaneous variability was pursued by means of a new bi-parametric statistical model that builds on a model previously proposed by the same authors. The statistical model is developed with direct reference to the Liu-Jordan clear sky theoretical expression but is not bound by a specific clear sky model; it accounts separately for the mean daily variability and for the variation of solar irradiance during the day by means of two corrective parameters. This new proposal allows for a better understanding of the physical phenomena and improves the effectiveness of statistical characterization and subsequent simulation of the introduced parameters to generate a synthetic solar irradiance time series. Furthermore, the analysis of the experimental distributions of the two parameters' data was developed, obtaining opportune fittings by means of parametric analytical distributions or mixtures of more than one distribution. Finally, the model was further improved toward the inclusion of weather prediction information in the solar irradiance forecasting stage, from the perspective of overcoming the limitations of purely statistical approaches and implementing a new tool in the frame of solar irradiance prediction accounting for weather predictions over different time horizons.

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Section: Month Experimental Parametric Ss Mae (Pu) Ss Rmse (Pu) Ss Mamentioning

confidence: 99%

“…For comparative purposes, the results of the proposed model have been compared with the evaluations performed in [24], which refer to a benchmarking exercise organized within the framework of the European project "WIRE" [25] with the purpose of evaluating the performance of state-of-the-art models for short-term renewable energy forecasting. More specifically, 10 different solar power forecasting methods were applied to historical data for 2010 and 2011 and with reference to the suburbs of the city of Milan in Northern Italy and to the suburban area of Catania in Southern Italy.…”

Section: Month Experimental Parametric Ss Mae (Pu) Ss Rmse (Pu) Ss Mamentioning

confidence: 99%

Solar Radiation Forecasting, Accounting for Daily Variability

2016

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“…Nowadays, renewable energy (RE) has become the main impetus of the energy sector, primarily because it is environment friendly, clean, and a secure energy source [1]. Among REs, photovoltaic (PV) power generation is one of the most promising technologies that can be utilized in industrial power systems and rural electrification [2].…”

Section: Introductionmentioning

confidence: 99%

A Nature-Inspired Optimization-Based Optimum Fuzzy Logic Photovoltaic Inverter Controller Utilizing an eZdsp F28335 Board

Mutlag

Mohamed²,

Shareef

2016

Energies

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Photovoltaic (PV) inverters essentially convert DC quantities, such as voltage and current, to AC quantities whose magnitude and frequency are controlled to obtain the desired output. Thus, the performance of an inverter depends on its controller. Therefore, an optimum fuzzy logic controller (FLC) design technique for PV inverters using a lightning search algorithm (LSA) is presented in this study. In a conventional FLC, the procedure for obtaining membership functions (MFs) is usually implemented using trial and error, which does not lead to satisfactory solutions in many cases. Therefore, this study presents a technique for obtaining MFs that avoids the exhaustive traditional trial-and-error procedure. This technique is implemented during the inverter design phase by generating adaptive MFs based on the evaluation results of the objective function formulated with LSA. The mean squared error (MSE) of the inverter output voltage is used as an objective function in this study. LSA optimizes the MFs such that the inverter provides the lowest MSE for the output voltage, and the performance of the PV inverter output is improved in terms of amplitude and frequency. First, the design procedure and accuracy of the optimum FLC are illustrated and investigated through simulations conducted in a MATLAB environment. The LSA-based FLC (LSA-FL) are compared with differential search algorithm (DSA)-based FLC (DSA-FL) and particle swarm optimization (PSO)-based FLC (PSO-FL). Finally, the robustness of the LSA-FL is further investigated with a hardware that is operated via an eZdsp F28335 control board. Simulation and experimental results show that the proposed controller can successfully obtain the desired output when different loads are connected to the system. The inverter also has a reasonably low steady-state error and fast response to reference variation.

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“…A comprehensive picture of the state-of-the-art of short-term wind power forecasting is presented in [2]. The article presents the results of a benchmarking exercise: a range of modelling approaches was evaluated using two different test cases over a time horizon ranging from 0 to 72 hours and 0 to 48 hours.…”

Section: Literature Reviewmentioning

confidence: 99%

The impact of power curve estimation on commercial wind power forecasts — An empirical analysis

Goretti¹,

Duffy²,

Lie

2017

2017 14th International Conference on the European Energy Market (EEM)

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Abstract-An increasing number of utilities participating in the energy market require short term (i.e. up to 48 hours) power forecasts for renewable generation in order to optimize technical and financial performances. As a result, a large number of forecast providers now operate in the marketplace, each using different methods and offering a wide range of services. This paper assesses five different day-ahead wind power forecasts generated by various service providers currently operating in the market, and compares their performance against the stateof-the-art of short-term wind power forecasting. The work focuses on how power curve estimations can introduce systematic errors that affect overall forecast performance. The results of the study highlight the importance of: accurately modelling the wind speedto-power output relationships at higher wind speeds; taking account of power curve trends when training models; and the need to incorporate long-term (months to years) power curve variability into the forecast updating process.Index Terms-wind power forecast; wind turbine power curve; short-term forecasting; forecast assessment; wind energy.

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The “Weather Intelligence for Renewable Energies” Benchmarking Exercise on Short-Term Forecasting of Wind and Solar Power Generation

Cited by 48 publications

References 22 publications

Solar Radiation Forecasting, Accounting for Daily Variability

Solar Radiation Forecasting, Accounting for Daily Variability

A Nature-Inspired Optimization-Based Optimum Fuzzy Logic Photovoltaic Inverter Controller Utilizing an eZdsp F28335 Board

The impact of power curve estimation on commercial wind power forecasts — An empirical analysis

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