Special Issue "Ensemble Forecasting Applied to Power Systems"

A special issue of Energies (ISSN 1996-1073). This special issue belongs to the section "Electrical Power and Energy System".

Deadline for manuscript submissions: closed (30 September 2019).

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A printed edition of this Special Issue is available here.

Special Issue Editors

Prof. Dr. Antonio Bracale
Website
Guest Editor
Department of Engineering, University of Naples Parthenope, Centro Direzionale Is. C4, 80143, Naples, Italy
Interests: power quality; energy forecasting; power system analysis
Special Issues and Collections in MDPI journals
Dr. Pasquale De Falco
Website SciProfiles
Guest Editor
Department of Engineering, University of Napoli Parthenope, Naples, Italy
Interests: energy forecasting; energy data analysis; renewable energy; dynamic rating of power system components; smart grids
Special Issues and Collections in MDPI journals

Special Issue Information

Dear Colleagues,

Forecasting is a crucial task in planning and managing modern power systems at various levels, such as transmission networks, distribution systems, and smart grids. Many important operations nowadays are scheduled and performed on the basis of predictions of several variables, such as non-controllable generation, loads, energy prices, and power quality indicators. Forecasts at different lead times, ranging from several minutes to several days, are needed in order to suit different applications and scenarios.

The application of forecasting techniques to power systems, in both deterministic and probabilistic frameworks, is yet to be fully explored.

Recent trends suggest the suitability of ensemble approaches in order to increase the versatility and robustness of forecasting systems. Stacking, boosting, and bagging techniques were successfully applied in several frameworks, and have recently started to attract the interest of power system practitioners. The subject is therefore worthy of further investigation.

This Special Issue addresses the development of new, advanced, ensemble forecasting methods applied to power systems. The Special Issue is opened to contributions, developed in both deterministic and probabilistic frameworks, which provide accurate forecasts in terms of spot values, prediction intervals, predictive distributions, predictive quantiles.

We are particularly interested in contributions dealing with forecasting power generated from renewable non-controllable sources (such as solar, wind, and tidal), loads (such as aggregated, individual, domestic, industrial, electrical vehicles), energy prices, and power quality indicators (such as voltage sag and harmonics). Further contributions with adequate level of innovation are encouraged as well.

All of the submitted contributions must demonstrate a theoretical sound framework, presenting also practical applications to actual scenarios.

Prof. Dr. Antonio Bracale
Dr. Pasquale De Falco
Guest Editors

Manuscript Submission Information

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Submitted manuscripts should not have been published previously, nor be under consideration for publication elsewhere (except conference proceedings papers). All manuscripts are thoroughly refereed through a single-blind peer-review process. A guide for authors and other relevant information for submission of manuscripts is available on the Instructions for Authors page. Energies is an international peer-reviewed open access semimonthly journal published by MDPI.

Please visit the Instructions for Authors page before submitting a manuscript. The Article Processing Charge (APC) for publication in this open access journal is 1800 CHF (Swiss Francs). Submitted papers should be well formatted and use good English. Authors may use MDPI's English editing service prior to publication or during author revisions.

Keywords

  • Ensemble forecasting
  • Renewable generation forecasting
  • Industrial load forecasting
  • Price forecasting
  • Power quality indices forecasting
  • Smart grids

Published Papers (7 papers)

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Research

Open AccessArticle
Characterising Seasonality of Solar Radiation and Solar Farm Output
Energies 2020, 13(2), 471; https://doi.org/10.3390/en13020471 - 18 Jan 2020
Cited by 1
Abstract
With the recent rapid increase in the use of roof top photovoltaic solar systems worldwide, and also, more recently, the dramatic escalation in building grid connected solar farms, especially in Australia, the need for more accurate methods of very short-term forecasting has become [...] Read more.
With the recent rapid increase in the use of roof top photovoltaic solar systems worldwide, and also, more recently, the dramatic escalation in building grid connected solar farms, especially in Australia, the need for more accurate methods of very short-term forecasting has become a focus of research. The International Energy Agency Tasks 46 and 16 have brought together groups of experts to further this research. In Australia, the Australian Renewable Energy Agency is funding consortia to improve the five minute forecasting of solar farm output, as this is the time scale of the electricity market. The first step in forecasting of either solar radiation or output from solar farms requires the representation of the inherent seasonality. One can characterise the seasonality in climate variables by using either a multiplicative or additive modelling approach. The multiplicative approach with respect to solar radiation can be done by calculating the clearness index, or alternatively estimating the clear sky index. The clearness index is defined as the division of the global solar radiation by the extraterrestrial radiation, a quantity determined only via astronomical formulae. To form the clear sky index one divides the global radiation by a clear sky model. For additive de-seasoning, one subtracts some form of a mean function from the solar radiation. That function could be simply the long term average at the time steps involved, or more formally the addition of terms involving a basis of the function space. An appropriate way to perform this operation is by using a Fourier series set of basis functions. This article will show that for various reasons the additive approach is superior. Also, the differences between the representation for solar energy versus solar farm output will be demonstrated. Finally, there is a short description of the subsequent steps in short-term forecasting. Full article
(This article belongs to the Special Issue Ensemble Forecasting Applied to Power Systems) Printed Edition available
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Open AccessArticle
A Novel Ensemble Algorithm for Solar Power Forecasting Based on Kernel Density Estimation
Energies 2020, 13(1), 216; https://doi.org/10.3390/en13010216 - 02 Jan 2020
Cited by 3
Abstract
A novel ensemble algorithm based on kernel density estimation (KDE) is proposed to forecast distributed generation (DG) from renewable energy sources (RES). The proposed method relies solely on publicly available historical input variables (e.g., meteorological forecasts) and the corresponding local output (e.g., recorded [...] Read more.
A novel ensemble algorithm based on kernel density estimation (KDE) is proposed to forecast distributed generation (DG) from renewable energy sources (RES). The proposed method relies solely on publicly available historical input variables (e.g., meteorological forecasts) and the corresponding local output (e.g., recorded power generation). Given a new case (with forecasted meteorological variables), the resulting power generation is forecasted. This is performed by calculating a KDE-based similarity index to determine a set of most similar cases from the historical dataset. Then, the outputs of the most similar cases are used to calculate an ensemble prediction. The method is tested using historical weather forecasts and recorded generation of a PV installation in Portugal. Despite only being given averaged data as input, the algorithm is shown to be capable of predicting uncertainties associated with high frequency weather variations, outperforming deterministic predictions based on solar irradiance forecasts. Moreover, the algorithm is shown to outperform a neural network (NN) in most test cases while being exceptionally faster (32 times). Given that the proposed model only relies on public locally-metered data, it is a convenient tool for DG owners/operators to effectively forecast their expected generation without depending on private/proprietary data or divulging their own. Full article
(This article belongs to the Special Issue Ensemble Forecasting Applied to Power Systems) Printed Edition available
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Open AccessArticle
Solar Power Interval Prediction via Lower and Upper Bound Estimation with a New Model Initialization Approach
Energies 2019, 12(21), 4146; https://doi.org/10.3390/en12214146 - 30 Oct 2019
Abstract
This paper proposes a new model initialization approach for solar power prediction interval based on the lower and upper bound estimation (LUBE) structure. The linear regression interval estimation (LRIE) was first used to initialize the prediction interval and the extreme learning machine auto [...] Read more.
This paper proposes a new model initialization approach for solar power prediction interval based on the lower and upper bound estimation (LUBE) structure. The linear regression interval estimation (LRIE) was first used to initialize the prediction interval and the extreme learning machine auto encoder (ELM-AE) is then employed to initialize the input weight matrix of the LUBE. Based on the initialized prediction interval and input weight matrix, the output weight matrix of the LUBE could be obtained, which was close to optimal values. The heuristic algorithm was employed to train the LUBE prediction model due to the invalidation of the traditional training approach. The proposed model initialization approach was compared with the point prediction initialization and random initialization approaches. To validate its performance, four heuristic algorithms, including particle swarm optimization (PSO), simulated annealing (SA), harmony search (HS), and differential evolution (DE), were introduced. Based on the experiment results, the proposed model initialization approach with different heuristic algorithms was better than the point prediction initialization and random initialization approaches. The PSO can obtain the best efficiency and effectiveness of the optimal solution searching in four heuristic algorithms. Besides, the ELM-AE can weaken the over-fitting phenomenon of the training model, which is brought in by the heuristic algorithm, and guarantee the model stable output. Full article
(This article belongs to the Special Issue Ensemble Forecasting Applied to Power Systems) Printed Edition available
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Open AccessArticle
Averaging Predictive Distributions Across Calibration Windows for Day-Ahead Electricity Price Forecasting
Energies 2019, 12(13), 2561; https://doi.org/10.3390/en12132561 - 03 Jul 2019
Cited by 3
Abstract
The recent developments in combining point forecasts of day-ahead electricity prices across calibration windows have provided an extremely simple, yet a very efficient tool for improving predictive accuracy. Here, we consider two novel extensions of this concept to probabilistic forecasting: one based on [...] Read more.
The recent developments in combining point forecasts of day-ahead electricity prices across calibration windows have provided an extremely simple, yet a very efficient tool for improving predictive accuracy. Here, we consider two novel extensions of this concept to probabilistic forecasting: one based on Quantile Regression Averaging (QRA) applied to a set of point forecasts obtained for different calibration windows, the other on a technique dubbed Quantile Regression Machine (QRM), which first averages these point predictions, then applies quantile regression to the combined forecast. Once computed, we combine the probabilistic forecasts across calibration windows by averaging probabilities of the corresponding predictive distributions. Our results show that QRM is not only computationally more efficient, but also yields significantly more accurate distributional predictions, as measured by the aggregate pinball score and the test of conditional predictive ability. Moreover, combining probabilistic forecasts brings further significant accuracy gains. Full article
(This article belongs to the Special Issue Ensemble Forecasting Applied to Power Systems) Printed Edition available
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Open AccessArticle
Combining Weather Stations for Electric Load Forecasting
Energies 2019, 12(8), 1510; https://doi.org/10.3390/en12081510 - 21 Apr 2019
Cited by 6
Abstract
Weather is a key factor affecting electricity demand. Many load forecasting models rely on weather variables. Weather stations provide point measurements of weather conditions in a service area. Since the load is spread geographically, a single weather station may not sufficiently explain the [...] Read more.
Weather is a key factor affecting electricity demand. Many load forecasting models rely on weather variables. Weather stations provide point measurements of weather conditions in a service area. Since the load is spread geographically, a single weather station may not sufficiently explain the variations of the load over a vast area. Therefore, a proper combination of multiple weather stations plays a vital role in load forecasting. This paper answers the question: given a number of weather stations, how should they be combined for load forecasting? Simple averaging has been a commonly used and effective method in the literature. In this paper, we compared the performance of seven alternative methods with simple averaging as the benchmark using the data of the Global Energy Forecasting Competition 2012. The results demonstrate that some of the methods outperform the benchmark in combining weather stations. In addition, averaging the forecasts from these methods outperforms most individual methods. Full article
(This article belongs to the Special Issue Ensemble Forecasting Applied to Power Systems) Printed Edition available
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Open AccessArticle
Microgrid-Level Energy Management Approach Based on Short-Term Forecasting of Wind Speed and Solar Irradiance
Energies 2019, 12(8), 1487; https://doi.org/10.3390/en12081487 - 19 Apr 2019
Cited by 5
Abstract
Background: The Distributed Energy Resources (DERs) are beneficial in reducing the electricity bills of the end customers in a smart community by enabling them to generate electricity for their own use. In the past, various studies have shown that owing to a lack [...] Read more.
Background: The Distributed Energy Resources (DERs) are beneficial in reducing the electricity bills of the end customers in a smart community by enabling them to generate electricity for their own use. In the past, various studies have shown that owing to a lack of awareness and connectivity, end customers cannot fully exploit the benefits of DERs. However, with the tremendous progress in communication technologies, the Internet of Things (IoT), Big Data (BD), machine learning, and deep learning, the potential benefits of DERs can be fully achieved, although a significant issue in forecasting the generated renewable energy is the intermittent nature of these energy resources. The machine learning and deep learning models can be trained using BD gathered over a long period of time to solve this problem. The trained models can be used to predict the generated energy through green energy resources by accurately forecasting the wind speed and solar irradiance. Methods: We propose an efficient approach for microgrid-level energy management in a smart community based on the integration of DERs and the forecasting wind speed and solar irradiance using a deep learning model. A smart community that consists of several smart homes and a microgrid is considered. In addition to the possibility of obtaining energy from the main grid, the microgrid is equipped with DERs in the form of wind turbines and photovoltaic (PV) cells. In this work, we consider several machine learning models as well as persistence and smart persistence models for forecasting of the short-term wind speed and solar irradiance. We then choose the best model as a baseline and compare its performance with our proposed multiheaded convolutional neural network model. Results: Using the data of San Francisco, New York, and Los Vegas from the National Solar Radiation Database (NSRDB) of the National Renewable Energy Laboratory (NREL) as a case study, the results show that our proposed model performed significantly better than the baseline model in forecasting the wind speed and solar irradiance. The results show that for the wind speed prediction, we obtained 44.94%, 46.12%, and 2.25% error reductions in root mean square error (RMSE), mean absolute error (MAE), and symmetric mean absolute percentage error (sMAPE), respectively. In the case of solar irradiance prediction, we obtained 7.68%, 54.29%, and 0.14% error reductions in RMSE, mean bias error (MBE), and sMAPE, respectively. We evaluate the effectiveness of the proposed model on different time horizons and different climates. The results indicate that for wind speed forecast, different climates do not have a significant impact on the performance of the proposed model. However, for solar irradiance forecast, we obtained different error reductions for different climates. This discrepancy is certainly due to the cloud formation processes, which are very different for different sites with different climates. Moreover, a detailed analysis of the generation estimation and electricity bill reduction indicates that the proposed framework will help the smart community to achieve an annual reduction of up to 38% in electricity bills by integrating DERs into the microgrid. Conclusions: The simulation results indicate that our proposed framework is appropriate for approximating the energy generated through DERs and for reducing the electricity bills of a smart community. The proposed framework is not only suitable for different time horizons (up to 4 h ahead) but for different climates. Full article
(This article belongs to the Special Issue Ensemble Forecasting Applied to Power Systems) Printed Edition available
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Open AccessArticle
Developing and Comparing Different Strategies for Combining Probabilistic Photovoltaic Power Forecasts in an Ensemble Method
Energies 2019, 12(6), 1011; https://doi.org/10.3390/en12061011 - 15 Mar 2019
Cited by 3
Abstract
Accurate probabilistic forecasts of renewable generation are drivers for operational and management excellence in modern power systems and for the sustainable integration of green energy. The combination of forecasts provided by different individual models may allow increasing the accuracy of predictions; however, in [...] Read more.
Accurate probabilistic forecasts of renewable generation are drivers for operational and management excellence in modern power systems and for the sustainable integration of green energy. The combination of forecasts provided by different individual models may allow increasing the accuracy of predictions; however, in contrast to point forecast combination, for which the simple weighted averaging is often a plausible solution, combining probabilistic forecasts is a much more challenging task. This paper aims at developing a new ensemble method for photovoltaic (PV) power forecasting, which combines the outcomes of three underlying probabilistic models (quantile k-nearest neighbors, quantile regression forests, and quantile regression) through a weighted quantile combination. Due to the challenges in combining probabilistic forecasts, the paper presents different combination strategies; the competing strategies are based on unconstrained, constrained, and regularized optimization problems for estimating the weights. The competing strategies are compared to individual forecasts and to several benchmarks, using the data published during the Global Energy Forecasting Competition 2014. Numerical experiments were run in MATLAB and R environments; the results suggest that unconstrained and Least Absolute Shrinkage and Selection Operator (LASSO)-regularized strategies exhibit the best performances for the datasets under study, outperforming the best competitors by 2.5 to 9% of the Pinball Score. Full article
(This article belongs to the Special Issue Ensemble Forecasting Applied to Power Systems) Printed Edition available
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