Probabilistic solar power forecasting in smart grids using distributed information

Bessa, Ricardo J.; Trindade, A.; Silva, Cátia S.P.; Miranda, Vladimiro

doi:10.1016/j.ijepes.2015.02.006

Cited by 120 publications

(56 citation statements)

References 23 publications

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“…PSA, being the use of a probabilistic model over various scenarios, foresees and evaluates various possible occurrences of an event in the future [28,29]. It is mostly used in the financial world to make extensive projections into the future.…”

Section: Probabilistic Scenario Analysis (Psa)mentioning

confidence: 99%

Short-Term Multiple Forecasting of Electric Energy Loads for Sustainable Demand Planning in Smart Grids for Smart Homes

Alani

Osunmakinde

2017

Sustainability

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Abstract:Energy consumption in the form of fuel or electricity is ubiquitous globally. Among energy types, electricity is crucial to human life in terms of cooking, warming and cooling of shelters, powering of electronic devices as well as commercial and industrial operations. Users of electronic devices sometimes consume fluctuating amounts of electricity generated from smart-grid infrastructure owned by the government or private investors. However, frequent imbalance is noticed between the demand and supply of electricity, hence effective planning is required to facilitate its distribution among consumers. Such effective planning is stimulated by the need to predict future consumption within a short period. Although several interesting classical techniques have been used for such predictions, they still require improvement for the purpose of reducing significant predictive errors when used for short-term load forecasting. This research develops a near-zero cooperative probabilistic scenario analysis and decision tree (PSA-DT) model to address the lacuna of enormous predictive error faced by the state-of-the-art models. The PSA-DT is based on a probabilistic technique in view of the uncertain nature of electricity consumption, complemented by a DT to reinforce the collaboration of the two techniques. Based on detailed experimental analytics on residential, commercial and industrial data loads, the PSA-DT model outperforms the state-of-the-art models in terms of accuracy to a near-zero error rate. This implies that its deployment for electricity demand planning will be of great benefit to various smart-grid operators and homes.

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Section: Probabilistic Scenario Analysis (Psa)mentioning

confidence: 99%

Short-Term Multiple Forecasting of Electric Energy Loads for Sustainable Demand Planning in Smart Grids for Smart Homes

Alani

Osunmakinde

2017

Sustainability

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“…However, how to utilize the forecasting results of each individual model in the total ensemble model is still a problem to be solved. Probabilistic forecasting approach is a common application of ensemble model as it can achieve the probability of different forecasting value, according to the outputs of multiple models [20,21]. In some situations, a unique result that can best provide the future information is still need, and a very common way is to calculate the average of the ensemble model outputs or use a mapping model to fuse the multiple results [17,22].…”

Section: Introductionmentioning

confidence: 99%

Time-Section Fusion Pattern Classification Based Day-Ahead Solar Irradiance Ensemble Forecasting Model Using Mutual Iterative Optimization

et al. 2018

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Accurate solar PV power forecasting can provide expected future PV output power so as to help the system operator to dispatch traditional power plants to maintain the balance between supply and demand sides. However, under non-stationary weather conditions, such as cloudy or partly cloudy days, the variability of solar irradiance makes the accurate PV power forecasting a very hard task. Ensemble forecasting based on multiple models established by different theory has been proved as an effective means on improving forecasting accuracy. Classification modeling according to different patterns could reduce the complexity and difficulty of intro-class data fitting so as to improve the forecasting accuracy as well. When combining the two above points and focusing on the different fusion pattern specifically in terms of hourly time dimension, a time-section fusion pattern classification based day-ahead solar irradiance ensemble forecasting model using mutual iterative optimization is proposed, which contains multiple forecasting models based on wavelet decomposition (WD), fusion pattern classification model, and fusion models corresponding to each fusion pattern. First, the solar irradiance is forecasted using WD based models at different WD level. Second, the fusion pattern classification recognition model is trained and then applied to recognize the different fusion pattern at each hourly time section. At last, the final forecasting result is obtained using the optimal fusion model corresponding to the data fusion pattern. In addition, a mutual iterative optimization framework for the pattern classification and data fusion models is also proposed to improve the model's performance. Simulations show that the mutual iterative optimization framework can effectively enhance the performance and coordination of pattern classification and data fusion models. The accuracy of the proposed solar irradiance day-ahead ensemble forecasting model is verified when compared with a standard Artificial Neural Network (ANN) forecasting model, five WD based models and a single ensemble forecasting model without time-section fusion classification.

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“…Some common usages of AMs are for operational precipitation forecasting (e.g., Guilbaud 1997;Bontron and Obled 2005;Hamill and Whitaker 2006;Bliefernicht 2010;Marty et al 2012;Horton et al 2012;Hamill et al 2015;Ben Daoud et al 2016), or more recently for precipitation downscaling in a climate perspective (e.g., Radanovics et al 2013;Chardon et al 2014;Dayon et al 2015). However, AMs or equivalent methods are also employed to predict temperatures (Radinović 1975;Woodcock 1980;Kruizinga and Murphy 1983;Delle Monache et al 2013;Caillouet et al 2016), wind (Gordon 1987;Delle Monache et al 2013, 2011Vanvyve et al 2015;Alessandrini et al 2015b;Junk et al 2015a,b), solar power Bessa et al 2015), snow avalanches (Obled and Good 1980;Bolognesi 1993), insolation (Bois et al 1981), and the trajectories of tropical cyclones (Keenan and Woodcock 1981;Sievers et al 2000;Fraedrich et al 2003).…”

Section: Introductionmentioning

confidence: 99%

Global Optimization of an Analog Method by Means of Genetic Algorithms

Horton

Jaboyedoff

Obled

2017

Monthly Weather Review

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Analog methods are based on a statistical relationship between synoptic meteorological variables (predictors) and local weather (predictand, to be predicted). This relationship is defined by several parameters, which are often calibrated by means of a semiautomatic sequential procedure. This calibration approach is fast, but has strong limitations. It proceeds through successive steps, and thus cannot handle all parameter dependencies. Furthermore, it cannot automatically optimize some parameters, such as the selection of pressure levels and temporal windows (hours of the day) at which the predictors are compared. To overcome these limitations, the global optimization technique of genetic algorithms is considered, which can jointly optimize all parameters of the method, and get closer to a global optimum, by taking into account the dependencies of the parameters. Moreover, it can objectively calibrate parameters that were previously assessed manually and can take into account new degrees of freedom. However, genetic algorithms must be tailored to the problem under consideration. Multiple combinations of algorithms were assessed, and new algorithms were developed (e.g., the chromosome of adaptive search radius, which is found to be very robust), in order to provide recommendations regarding the use of genetic algorithms for optimizing several variants of analog methods. A global optimization approach provides new perspectives for the improvement of analog methods, and for their application to new regions or new predictands.

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Probabilistic solar power forecasting in smart grids using distributed information

Abstract: 2016-11-02T18:49:00

Cited by 120 publications

References 23 publications

Short-Term Multiple Forecasting of Electric Energy Loads for Sustainable Demand Planning in Smart Grids for Smart Homes

Short-Term Multiple Forecasting of Electric Energy Loads for Sustainable Demand Planning in Smart Grids for Smart Homes

Time-Section Fusion Pattern Classification Based Day-Ahead Solar Irradiance Ensemble Forecasting Model Using Mutual Iterative Optimization

Global Optimization of an Analog Method by Means of Genetic Algorithms

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