Review on probabilistic forecasting of photovoltaic power production and electricity consumption

Meer, Dennis van der; Widén, Joakim; Munkhammar, Joakim

doi:10.1016/j.rser.2017.05.212

Cited by 353 publications

(162 citation statements)

References 108 publications

Supporting

Mentioning

161

Contrasting

Order By: Relevance

“…A frequent baseline model used for deterministic forecasts is the simple persistence model [29]. This model assumes that the conditions at time t persist at least up to the period of forecasting interest at time t + h. The persistence model is defined as…”

Section: Baseline Modelmentioning

confidence: 99%

“…where N is the length of the forecasted time series, x t is the forecasted value and x t is the observed value. As MAE is only a valid error measure if one can assume that for the forecasted distribution the mean is equal to the median (which might be too restrictive), an alternative is the root mean square error (RMSE), i.e., the square root of the average squared differences [29,41]:…”

Section: Error Measuresmentioning

confidence: 99%

See 1 more Smart Citation

Forecasting in Blockchain-Based Local Energy Markets

Kostmann

Härdle

2019

Energies

View full text Add to dashboard Cite

Increasingly volatile and distributed energy production challenges traditional mechanisms to manage grid loads and price energy. Local energy markets (LEMs) may be a response to those challenges as they can balance energy production and consumption locally and may lower energy costs for consumers. Blockchain-based LEMs provide a decentralized market to local energy consumer and prosumers. They implement a market mechanism in the form of a smart contract without the need for a central authority coordinating the market. Recently proposed blockchain-based LEMs use auction designs to match future demand and supply. Thus, such blockchain-based LEMs rely on accurate short-term forecasts of individual households' energy consumption and production. Often, such accurate forecasts are simply assumed to be given. The present research tested this assumption by first evaluating the forecast accuracy achievable with state-of-the-art energy forecasting techniques for individual households and then, assessing the effect of prediction errors on market outcomes in three different supply scenarios. The evaluation showed that, although a LASSO regression model is capable of achieving reasonably low forecasting errors, the costly settlement of prediction errors can offset and even surpass the savings brought to consumers by a blockchain-based LEM. This shows that, due to prediction errors, participation in LEMs may be uneconomical for consumers, and thus, has to be taken into consideration for pricing mechanisms in blockchain-based LEMs.

show abstract

Section: Baseline Modelmentioning

confidence: 99%

Section: Error Measuresmentioning

confidence: 99%

Forecasting in Blockchain-Based Local Energy Markets

Kostmann

Härdle

2019

Energies

View full text Add to dashboard Cite

show abstract

“…For example Antonanzas et al (2016) discussed papers using neural networks, random forests, support vector machines and nearest neighbours for forecasting solar power. Van der Meer et al (2018) reviewed papers using quantile regression, quantile regression forests, Gaussian processes, bootstrapping, lower upper bound estimate, gradient boosting, kernel density estimation, nearest neighbours and an analog ensemble for forecasting solar power. Voyant et al (2017) discussed papers using linear regression, generalized linear models, neural networks, support vector machines, decision tree learning, nearest neighbours and Markov chains for forecasting solar radiation.…”

Section: Introductionmentioning

confidence: 99%

Comparison of statistical post-processing methods for probabilistic NWP forecasts of solar radiation

et al. 2019

View full text Add to dashboard Cite

The increased usage of solar energy places additional importance on forecasts of solar radiation. Solar panel power production is primarily driven by the amount of solar radiation and it is therefore important to have accurate forecasts of solar radiation. Accurate forecasts that also give information on the * Corresponding author ranked probability skill score, the Brier skill score and reliability diagrams. We find that quantile regression and generalized random forests generally perform best. In (near) clear sky conditions the non-parametric methods have more skill than the parametric ones.

show abstract

“…The literature on the probabilistic forecasting of PV production is less rich than that on wind generation or grid load forecasting. A very recent and accurate review can be found at [7].…”

Section: Introductionmentioning

confidence: 99%

Quantile Regression Post-Processing of Weather Forecast for Short-Term Solar Power Probabilistic Forecasting

Massidda

Marrocu

2018

Energies

View full text Add to dashboard Cite

Abstract:The inclusion of photo-voltaic generation in the distribution grid poses technical difficulties related to the variability of the solar source and determines the need for Probabilistic Forecasting procedures (PF). This work describes a new approach for PF based on quantile regression using the Gradient-Boosted Regression Trees (GBRT) method fed by numerical weather forecasts of the European Centre for Medium Range Weather Forecast (ECMWF) Integrated Forecasting System (IFS) and Ensemble Prediction System (EPS). The proposed methodology is compared with the forecasts obtained with Quantile Regression using only IFS forecasts (QR), with the uncalibrated EPS forecasts and with the EPS forecasts calibrated with a Variance Deficit (VD) procedure. The proposed methodology produces forecasts with a temporal resolution equal to or better than the meteorological forecast (1 h for the IFS and 3 h for EPS) and, in the case examined, is able to provide higher performances than those obtained with the other methods over a forecast horizon of up to 72 h.

show abstract

Review on probabilistic forecasting of photovoltaic power production and electricity consumption

Cited by 353 publications

References 108 publications

Forecasting in Blockchain-Based Local Energy Markets

Forecasting in Blockchain-Based Local Energy Markets

Comparison of statistical post-processing methods for probabilistic NWP forecasts of solar radiation

Quantile Regression Post-Processing of Weather Forecast for Short-Term Solar Power Probabilistic Forecasting

Contact Info

Product

Resources

About