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Applications of Artificial Intelligence in Renewable Energy

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A special issue of Energies (ISSN 1996-1073). This special issue belongs to the section "A: Sustainable Energy".

Deadline for manuscript submissions: closed (31 March 2020) | Viewed by 44441

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Prof. Dr. Sue Ellen Haupt

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Research Applications Laboratory (RAL), National Center for Atmospheric Research, Boulder, CO, USA
Interests: meteorology; wind energy; artificial intelligence; renewable energy; boundary layer meteorology
Special Issues, Collections and Topics in MDPI journals

Dr. Tyler C. McCandless

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Research Applications Laboratory (RAL), National Center for Atmospheric Research, Boulder, CO, USA
Interests: machine learning techniques, wind and solar power prediction, energy risk modeling

Dr. David John Gagne II

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Computational and Information Systems Laboratory, Technology Development Division, National Center for Atmospheric Research, Boulder, CO, USA
Interests: meteorology, machine learning, artificial intelligence, severe weather, renewable energy

Special Issue Information

Dear colleagues,

The growth of installed renewable energy generation capacity has triggered a paradigm shift in the energy industry with a move from traditional baseload power generation sources of coal and nuclear energy to the now lower cost renewable energy resources of wind and solar power. However, this fundamental shift has widespread consequences in the energy industry, as traditional baseload generation is less variable due to weather dependence than renewable energy resources that are fundamentally driven by the weather. Additionally, the industry is changing from a market based on commodity pricing to a market based on technology solutions in order to integrate renewable energy. As the energy industry continues to utilize more variable generation sources, accurate forecasts of power generation and net load are becoming essential to maintain system reliability, minimize carbon emissions, and maximize renewable energy resources.

There are numerous complex, nonlinear interactions among multiple parameters controlling the integration of renewable energy into the electric grid. Artificial Intelligence approaches are being developed to produce more accurate predictions of renewable energy, including their generation and impacts on the electric grid such as net load forecasting, line loss predictions, maintaining system reliability, integrating hybrid solar and battery storage systems, and predicting equipment failure. Both fundamental and applied research are leveraging artificial intelligence to revolutionize the energy industry to utilize the capabilities of renewable energy.

This Special Issue seeks to contribute to advancing the generation capacity and integration of renewable energy into the electric grid with artificial intelligence. We invite papers on innovative Artificial Intelligence applications to renewable energy forecasting and integration, including reviews and case studies.

Prof. Dr. Sue Ellen Haupt
Dr. Tyler C. McCandless
Dr. David John Gagne II
Guest Editors

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Keywords

artificial intelligence
machine learning
renewable energy
solar power
wind power
data science
deep learning
artificial neural networks
computational intelligence
data mining
net load forecasting

Published Papers (11 papers)

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Research

17 pages, 5247 KiB

Open AccessArticle

Modeling of Interconnected Infrastructures with Unified Interface Design toward Smart Cities

by Hossam A. Gabbar

Energies 2021, 14(15), 4572; https://0-doi-org.brum.beds.ac.uk/10.3390/en14154572 - 28 Jul 2021

Cited by 4 | Viewed by 1833

Abstract

In recent years, there have been tendencies to enable smart cities with interconnected infrastructures and communities. Current engineering design and operation practices are limited to handling individual systems with modeling and simulation, as well as control systems. This paper presents a holistic approach with engineering practice to design and operate interconnected systems as part of smart cities. The approach is based on modeling individual physical systems and associated processes and identifying key performance indicators to evaluate each system and interconnected systems with an understanding of the coupling among systems to increase the overall performance of interconnected systems. The multi-objective optimization technique is proposed to achieve the best performance based on system design, control, and operation parameters. Due to the multidimensional nature of the interconnected systems, a unified interface system with modular design is proposed to achieve the highest overall performance of the interconnected systems with standardized interactions among state variables and performance measures. The proposed approach can allow dynamic updates of the interconnected systems based on model libraries of each system and process. A case study is presented of interconnected energy–water–transportation–waste facilities, whereby modeling is discussed, and performance measures are evaluated for different scenarios using the unified interface design. Full article

(This article belongs to the Special Issue Applications of Artificial Intelligence in Renewable Energy)

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18 pages, 3820 KiB

Open AccessFeature PaperArticle

The Schaake Shuffle Technique to Combine Solar and Wind Power Probabilistic Forecasting

by Stefano Alessandrini and Tyler McCandless

Energies 2020, 13(10), 2503; https://0-doi-org.brum.beds.ac.uk/10.3390/en13102503 - 15 May 2020

Cited by 16 | Viewed by 2882

Abstract

One way to mitigate the variability of wind and solar power generation is to install the corresponding plants in nearby locations. For example, in Kuwait, the facility at Shagaya Renewable Energy Park is located in a desert area with both photovoltaic panels and wind turbines that allow the continuous generation of renewable energy throughout the day. The National Center for Atmospheric Research (NCAR) has developed a system to generate probabilistic wind and solar predictions for the Shagaya facility. These predictions are based on the analog ensemble technique that post-processes the wind speed and solar irradiance predictions based on a combination of multiple models including the Weather Research and Forecasting (WRF) numerical model. The ensemble forecasts have 20 members and are generated independently at each wind and solar power production facility. Here we present a method based on the Schaake Shuffle (SS) technique to pair the ensemble members from the independent systems to obtain a unique ensemble prediction of the aggregated wind and solar generation. After reordering through the SS technique, the corresponding paired solar and wind power members can be summed to build a unique ensemble of combined generation that is statistically consistent, as verified by the presented metrics. Full article

(This article belongs to the Special Issue Applications of Artificial Intelligence in Renewable Energy)

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12 pages, 5167 KiB

Open AccessArticle

Advanced PV Performance Modelling Based on Different Levels of Irradiance Data Accuracy

by Julián Ascencio-Vásquez, Jakob Bevc, Kristjan Reba, Kristijan Brecl, Marko Jankovec and Marko Topič

Energies 2020, 13(9), 2166; https://0-doi-org.brum.beds.ac.uk/10.3390/en13092166 - 1 May 2020

Cited by 17 | Viewed by 3549

Abstract

In photovoltaic (PV) systems, energy yield is one of the essential pieces of information to the stakeholders (grid operators, maintenance operators, financial units, etc.). The amount of energy produced by a photovoltaic system in a specific time period depends on the weather conditions, including snow and dust, the actual PV modules’ and inverters’ efficiency and balance-of-system losses. The energy yield can be estimated by using empirical models with accurate input data. However, most of the PV systems do not include on-site high-class measurement devices for irradiance and other weather conditions. For this reason, the use of reanalysis-based or satellite-based data is currently of significant interest in the PV community and combining the data with decomposition and transposition irradiance models, the actual Plane-of-Array operating conditions can be determined. In this paper, we are proposing an efficient and accurate approach for PV output energy modelling by combining a new data filtering procedure and fast machine learning algorithm Light Gradient Boosting Machine (LightGBM). The applicability of the procedure is presented on three levels of irradiance data accuracy (low, medium, and high) depending on the source or modelling used. A new filtering algorithm is proposed to exclude erroneous data due to system failures or unreal weather conditions (i.e., shading, partial snow coverage, reflections, soiling deposition, etc.). The cleaned data is then used to train three empirical models and three machine learning approaches, where we emphasize the advantages of the LightGBM. The experiments are carried out on a 17 kW roof-top PV system installed in Ljubljana, Slovenia, in a temperate climate zone. Full article

(This article belongs to the Special Issue Applications of Artificial Intelligence in Renewable Energy)

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42 pages, 2487 KiB

Open AccessFeature PaperArticle

Self-Learning Data-Based Models as Basis of a Universally Applicable Energy Management System

by Malin Lachmann, Jaime Maldonado, Wiebke Bergmann, Francesca Jung, Markus Weber and Christof Büskens

Energies 2020, 13(8), 2084; https://0-doi-org.brum.beds.ac.uk/10.3390/en13082084 - 21 Apr 2020

Cited by 6 | Viewed by 3021

Abstract

In the transfer from fossil fuels to renewable energies, grid operators, companies and farms develop an increasing interest in smart energy management systems which can reduce their energy expenses. This requires sufficiently detailed models of the underlying components and forecasts of generation and consumption over future time horizons. In this work, it is investigated via a real-world case study how data-based methods based on regression and clustering can be applied to this task, such that potentially extensive effort for physical modeling can be decreased. Models and automated update mechanisms are derived from measurement data for a photovoltaic plant, a heat pump, a battery storage, and a washing machine. A smart energy system is realized in a real household to exploit the resulting models for minimizing energy expenses via optimization of self-consumption. Experimental data are presented that illustrate the models’ performance in the real-world system. The study concludes that it is possible to build a smart adaptive forecast-based energy management system without expert knowledge of detailed physics of system components, but special care must be taken in several aspects of system design to avoid undesired effects which decrease the overall system performance. Full article

(This article belongs to the Special Issue Applications of Artificial Intelligence in Renewable Energy)

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23 pages, 8222 KiB

Open AccessArticle

Combining Artificial Intelligence with Physics-Based Methods for Probabilistic Renewable Energy Forecasting

by Sue Ellen Haupt, Tyler C. McCandless, Susan Dettling, Stefano Alessandrini, Jared A. Lee, Seth Linden, William Petzke, Thomas Brummet, Nhi Nguyen, Branko Kosović, Gerry Wiener, Tahani Hussain and Majed Al-Rasheedi

Energies 2020, 13(8), 1979; https://0-doi-org.brum.beds.ac.uk/10.3390/en13081979 - 16 Apr 2020

Cited by 40 | Viewed by 7031

Abstract

A modern renewable energy forecasting system blends physical models with artificial intelligence to aid in system operation and grid integration. This paper describes such a system being developed for the Shagaya Renewable Energy Park, which is being developed by the State of Kuwait. The park contains wind turbines, photovoltaic panels, and concentrated solar renewable energy technologies with storage capabilities. The fully operational Kuwait Renewable Energy Prediction System (KREPS) employs artificial intelligence (AI) in multiple portions of the forecasting structure and processes, both for short-range forecasting (i.e., the next six hours) as well as for forecasts several days out. These AI methods work synergistically with the dynamical/physical models employed. This paper briefly describes the methodology used for each of the AI methods, how they are blended, and provides a preliminary assessment of their relative value to the prediction system. Each operational AI component adds value to the system. KREPS is an example of a fully integrated state-of-the-science forecasting system for renewable energy. Full article

(This article belongs to the Special Issue Applications of Artificial Intelligence in Renewable Energy)

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17 pages, 4721 KiB

Open AccessArticle

Reconstruction of Multidecadal Country-Aggregated Hydro Power Generation in Europe Based on a Random Forest Model

by Linh T. T. Ho, Laurent Dubus, Matteo De Felice and Alberto Troccoli

Energies 2020, 13(7), 1786; https://0-doi-org.brum.beds.ac.uk/10.3390/en13071786 - 7 Apr 2020

Cited by 13 | Viewed by 3533

Abstract

Hydro power can provide a source of dispatchable low-carbon electricity and a storage solution in a climate-dependent energy mix with high shares of wind and solar production. Therefore, understanding the effect climate has on hydro power generation is critical to ensure a stable energy supply, particularly at a continental scale. Here, we introduce a framework using climate data to model hydro power generation at the country level based on a machine learning method, the random forest model, to produce a publicly accessible hydro power dataset from 1979 to present for twelve European countries. In addition to producing a consistent European hydro power generation dataset covering the past 40 years, the specific novelty of this approach is to focus on the lagged effect of climate variability on hydro power. Specifically, multiple lagged values of temperature and precipitation are used. Overall, the model shows promising results, with the correlation values ranging between 0.85 and 0.98 for run-of-river and between 0.73 and 0.90 for reservoir-based generation. Compared to the more standard optimal lag approach the normalised mean absolute error reduces by an average of 10.23% and 5.99%, respectively. The model was also implemented over six Italian bidding zones to also test its skill at the sub-country scale. The model performance is only slightly degraded at the bidding zone level, but this also depends on the actual installed capacity, with higher capacities displaying higher performance. The framework and results presented could provide a useful reference for applications such as pan-European (continental) hydro power planning and for system adequacy and extreme events assessments. Full article

(This article belongs to the Special Issue Applications of Artificial Intelligence in Renewable Energy)

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15 pages, 3845 KiB

Open AccessArticle

Examining the Potential of a Random Forest Derived Cloud Mask from GOES-R Satellites to Improve Solar Irradiance Forecasting

by Tyler McCandless and Pedro Angel Jiménez

Energies 2020, 13(7), 1671; https://0-doi-org.brum.beds.ac.uk/10.3390/en13071671 - 3 Apr 2020

Cited by 17 | Viewed by 3092

Abstract

In order for numerical weather prediction (NWP) models to correctly predict solar irradiance reaching the earth’s surface for more accurate solar power forecasting, it is important to initialize the NWP model with accurate cloud information. Knowing where the clouds are located is the first step. Using data from geostationary satellites is an attractive possibility given the low latencies and high spatio-temporal resolution provided nowadays. Here, we explore the potential of utilizing the random forest machine learning method to generate the cloud mask from GOES-16 radiances. We first perform a predictor selection process to determine the optimal predictor set for the random forest predictions of the horizontal cloud fraction and then determine the appropriate threshold to generate the cloud mask prediction. The results show that the random forest method performs as well as the GOES-16 level 2 clear sky mask product with the ability to customize the threshold for under or over predicting cloud cover. Further developments to enhance the cloud mask estimations for improved short-term solar irradiance and power forecasting with the MAD-WRF NWP model are discussed. Full article

(This article belongs to the Special Issue Applications of Artificial Intelligence in Renewable Energy)

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16 pages, 4870 KiB

Open AccessArticle

A Comprehensive Wind Power Forecasting System Integrating Artificial Intelligence and Numerical Weather Prediction

by Branko Kosovic, Sue Ellen Haupt, Daniel Adriaansen, Stefano Alessandrini, Gerry Wiener, Luca Delle Monache, Yubao Liu, Seth Linden, Tara Jensen, William Cheng, Marcia Politovich and Paul Prestopnik

Energies 2020, 13(6), 1372; https://0-doi-org.brum.beds.ac.uk/10.3390/en13061372 - 16 Mar 2020

Cited by 46 | Viewed by 5781

Abstract

The National Center for Atmospheric Research (NCAR) recently updated the comprehensive wind power forecasting system in collaboration with Xcel Energy addressing users’ needs and requirements by enhancing and expanding integration between numerical weather prediction and machine-learning methods. While the original system was designed with the primary focus on day-ahead power prediction in support of power trading, the enhanced system provides short-term forecasting for unit commitment and economic dispatch, uncertainty quantification in wind speed prediction with probabilistic forecasting, and prediction of extreme events such as icing. Furthermore, the empirical power conversion machine-learning algorithms now use a quantile approach to data quality control that has improved the accuracy of the methods. Forecast uncertainty is quantified using an analog ensemble approach. Two methods of providing short-range ramp forecasts are blended: the variational doppler radar analysis system and an observation-based expert system. Extreme events, specifically changes in wind power due to high winds and icing, are now forecasted by combining numerical weather prediction and a fuzzy logic artificial intelligence system. These systems and their recent advances are described and assessed. Full article

(This article belongs to the Special Issue Applications of Artificial Intelligence in Renewable Energy)

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14 pages, 4952 KiB

Open AccessArticle

Comparison of Implicit vs. Explicit Regime Identification in Machine Learning Methods for Solar Irradiance Prediction

by Tyler McCandless, Susan Dettling and Sue Ellen Haupt

Energies 2020, 13(3), 689; https://0-doi-org.brum.beds.ac.uk/10.3390/en13030689 - 5 Feb 2020

Cited by 18 | Viewed by 3026

Abstract

This work compares the solar power forecasting performance of tree-based methods that include implicit regime-based models to explicit regime separation methods that utilize both unsupervised and supervised machine learning techniques. Previous studies have shown an improvement utilizing a regime-based machine learning approach in a climate with diverse cloud conditions. This study compares the machine learning approaches for solar power prediction at the Shagaya Renewable Energy Park in Kuwait, which is in an arid desert climate characterized by abundant sunshine. The regime-dependent artificial neural network models undergo a comprehensive parameter and hyperparameter tuning analysis to minimize the prediction errors on a test dataset. The final results that compare the different methods are computed on an independent validation dataset. The results show that the tree-based methods, the regression model tree approach, performs better than the explicit regime-dependent approach. These results appear to be a function of the predominantly sunny conditions that limit the ability of an unsupervised technique to separate regimes for which the relationship between the predictors and the predictand would differ for the supervised learning technique. Full article

(This article belongs to the Special Issue Applications of Artificial Intelligence in Renewable Energy)

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19 pages, 1202 KiB

Open AccessArticle

Improving Prediction Intervals Using Measured Solar Power with a Multi-Objective Approach

by Ricardo Aler, Javier Huertas-Tato, José M. Valls and Inés M. Galván

Energies 2019, 12(24), 4713; https://0-doi-org.brum.beds.ac.uk/10.3390/en12244713 - 10 Dec 2019

Cited by 3 | Viewed by 2128

Abstract

Prediction Intervals are pairs of lower and upper bounds on point forecasts and are useful to take into account the uncertainty on predictions. This article studies the influence of using measured solar power, available at prediction time, on the quality of prediction intervals. While previous studies have suggested that using measured variables can improve point forecasts, not much research has been done on the usefulness of that additional information, so that prediction intervals with less uncertainty can be obtained. With this aim, a multi-objective particle swarm optimization method was used to train neural networks whose outputs are the interval bounds. The inputs to the network used measured solar power in addition to hourly meteorological forecasts. This study was carried out on data from three different locations and for five forecast horizons, from 1 to 5 h. The results were compared with two benchmark methods (quantile regression and quantile regression forests). The Wilcoxon test was used to assess statistical significance. The results show that using measured power reduces the uncertainty associated to the prediction intervals, but mainly for the short forecasting horizons. Full article

(This article belongs to the Special Issue Applications of Artificial Intelligence in Renewable Energy)

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17 pages, 3203 KiB

Open AccessFeature PaperArticle

Assessing Evidence for Weather Regimes Governing Solar Power Generation in Kuwait

by Mari R. Tye, Sue Ellen Haupt, Eric Gilleland, Christina Kalb and Tara Jensen

Energies 2019, 12(23), 4409; https://0-doi-org.brum.beds.ac.uk/10.3390/en12234409 - 20 Nov 2019

Cited by 5 | Viewed by 3003

Abstract

With electricity representing around 20% of the global energy demand, and increasing support for renewable sources of electricity, there is also an escalating need to improve solar forecasts to support power management. While considerable research has been directed to statistical methods to improve solar power forecasting, few have employed finite mixture distributions. A statistically-objective classification of the overall sky condition may lead to improved forecasts. Combining information from the synoptic driving conditions for daily variability with local processes controlling subdaily fluctuations could assist with forecast validation and enhancement where few observations are available. Gaussian mixture models provide a statistical learning approach to automatically identify prevalent sky conditions (clear, semi-cloudy, and cloudy) and explore associated weather patterns. Here a first stage in the development of such a model is presented: examining whether there is sufficient information in the large-scale environment to identify days with clear, semi-cloudy, or cloudy conditions. A three-component Gaussian distribution is developed that reproduces the observed multimodal peaks in sky clearness indices, and their temporal distribution. Posterior probabilities from the fitted mixture distributions are used to identify periods of clear, partially-cloudy, and cloudy skies. Composites of low-level (850 hPa) humidity and winds for each of the mixture components reveal three patterns associated with the typical synoptic conditions governing the sky clarity, and hence, potential solar power. Full article

(This article belongs to the Special Issue Applications of Artificial Intelligence in Renewable Energy)

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