Evaluating Combined Load Forecasting in Large Power Systems and Smart Grids

Borges, Cruz E.; Penya, Yoseba K.; Fernández, Iván

doi:10.1109/tii.2012.2219063

Cited by 104 publications

(52 citation statements)

References 47 publications

Supporting

Mentioning

Contrasting

Order By: Relevance

“…In addition, another n apa particles meeting the requirements will be randomly added. According to Equation (28), n pa particles are selected from the n pa + n apa particles based on the affinity and concentration of antibody and antigen.…”

Section: Implementation Steps Of Forecast Model Weight Determination mentioning

confidence: 99%

“…The exponential smoothing method reported by Weron et al [23], the gray forecast method reported by Li et al [24] based on time trend extrapolation reported by Ismail et al [25], the clustering forecast method reported by Kodogiannis et al [26] and multiple regression analysis method reported by Hong et al [27] based on the load related factor analysis cannot ensure the satisfactory result in any case. In order to make full use of the advantages and the contained information of each single forecast model, combination forecast [28][29][30][31][32][33] is an effective method. The question of how to determine the weight assignment of single forecast method is a difficult point in combination forecasting.…”

Section: Introductionmentioning

confidence: 99%

See 1 more Smart Citation

A Robust Weighted Combination Forecasting Method Based on Forecast Model Filtering and Adaptive Variable Weight Determination

Ding

et al. 2015

Energies

View full text Add to dashboard Cite

Medium-and-long-term load forecasting plays an important role in energy policy implementation and electric department investment decision. Aiming to improve the robustness and accuracy of annual electric load forecasting, a robust weighted combination load forecasting method based on forecast model filtering and adaptive variable weight determination is proposed. Similar years of selection is carried out based on the similarity between the history year and the forecast year. The forecast models are filtered to select the better ones according to their comprehensive validity degrees. To determine the adaptive variable weight of the selected forecast models, the disturbance variable is introduced into Immune Algorithm-Particle Swarm Optimization (IA-PSO) and the adaptive adjustable strategy of particle search speed is established. Based on the forecast model weight determined by improved IA-PSO, the weighted combination forecast of annual electric load is obtained. The given case study illustrates the correctness and feasibility of the proposed method.

show abstract

Section: Implementation Steps Of Forecast Model Weight Determination mentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

A Robust Weighted Combination Forecasting Method Based on Forecast Model Filtering and Adaptive Variable Weight Determination

Ding

et al. 2015

Energies

View full text Add to dashboard Cite

show abstract

“…-Model Combination: another option is to group all the predictions issued by the forecasters in order to build a more robust forecast. We have addressed this strategy in [13,32].…”

Section: Data Distribution Service (Dds)mentioning

confidence: 99%

“…These tables also contain an estimation of the expected value of the error for every dataset under the hypothesis that ε follows a Gaussian Random Variable (column Expected MAPE), which according to our experiments is a fair assumption. Please note that in [32] we present a detailed description of the estimation process. Moreover, some TSOs publish their own STLF so we can calculate their error in the same way as with our predictions.…”

Section: Datasetsmentioning

confidence: 99%

Assessing Tolerance-Based Robust Short-Term Load Forecasting in Buildings

et al. 2013

View full text Add to dashboard Cite

Short-term load forecasting (STLF) in buildings differs from its broader counterpart in that the load to be predicted does not seem to be stationary, seasonal and regular but, on the contrary, it may be subject to sudden changes and variations on its consumption behaviour. Classical STLF methods do not react fast enough to these perturbations (i.e., they are not robust) and the literature on building STLF has not yet explored this area. Hereby, we evaluate a well-known post-processing method (Learning Window Reinitialization) applied to two broadly-used STLF algorithms (Autoregressive Model and Support Vector Machines) in buildings to check their adaptability and robustness. We have tested the proposed method with real-world data and our results state that this methodology is especially suited for buildings with non-regular consumption profiles, as classical STLF methods are enough to model regular-profiled ones.

show abstract

“…However, single predicting model to forecast PV output power has its characteristics, drawbacks and forecasting problems. Combination forecasting is an effective way to improve prediction accuracy by making full use of the contained information of each single forecast model [13], [14]. The issue of how to determine the weight of single forecast method is a difficult point in combination forecasting.…”

Section: Introductionmentioning

confidence: 99%

Photovoltaic power forecasting with a rough set combination method

Yang

Ren

Yue

2016

2016 UKACC 11th International Conference on Control (CONTROL)

View full text Add to dashboard Cite

Abstract-One major challenge with integrating photovoltaic (PV) systems into the grid is that its power generation is intermittent and uncontrollable due to the variation in solar radiation. An accurate PV power forecasting is crucial to the safe operation of the grid connected PV power station. In this work, a combined model with three different PV forecasting models is proposed based on a rough set method. The combination weights for each individual model are determined by rough set method according to its significance degree of condition attribute. The three different forecasting models include a past-power persistence model, a support vector machine (SVM) model and a similar data prediction model. The case study results show that, in comparison with each single forecasting model, the proposed combined model can identify the amount of useful information in a more effective manner.

show abstract

Evaluating Combined Load Forecasting in Large Power Systems and Smart Grids

Cited by 104 publications

References 47 publications

A Robust Weighted Combination Forecasting Method Based on Forecast Model Filtering and Adaptive Variable Weight Determination

A Robust Weighted Combination Forecasting Method Based on Forecast Model Filtering and Adaptive Variable Weight Determination

Assessing Tolerance-Based Robust Short-Term Load Forecasting in Buildings

Photovoltaic power forecasting with a rough set combination method

Contact Info

Product

Resources

About