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... [more]

Cyberspace has become an indispensable factor for all areas of the modern world. The world is becoming more and more dependent on the internet for everyday living. The increasing dependency on the internet has also widened the risks of malicious threats. On account of growing cybersecurity risks, cybersecurity has become the most pivotal element in the cyber world to battle against all cyber threats, attacks, and frauds. The expanding cyberspace is highly exposed to the intensifying possibility of being attacked by interminable cyber threats. The objective of this survey is to bestow a brief review of different machine learning (ML) techniques to get to the bottom of all the developments made in detection methods for potential cybersecurity risks. These cybersecurity risk detection methods mainly comprise of fraud detection, intrusion detection, spam detection, and malware detection. In this review paper, we build upon the existing literature of applications of ML models in cybersecuri... [more]

Surface chemical composition of bimetallic catalysts can differ from the bulk composition because of the segregation of the alloy components. Thus, it is very useful to know how the different components are arranged on the surface of catalysts to gain a fundamental understanding of the catalysis occurring on bimetallic surfaces. First-principles density functional theory (DFT) calculations can provide deeper insight into the surface segregation behavior and help understand the surface composition on bimetallic surfaces. However, the DFT calculations are computationally demanding and require large computing platforms. In this regard, statistical/machine learning methods provide a quick and alternative approach to study materials properties. Here, we trained previously reported surface segregation energies on low index surfaces of bimetallic catalysts using various linear and non-linear statistical methods to find a correlation between surface segregation energies and elemental propertie... [more]

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 da... [more]

Dynamic Mode Decomposition (DMD) techniques have risen as prominent feature identification methods in the field of fluid dynamics. Any of the multiple variables of the DMD method allows to identify meaningful features from either experimental or numerical flow data on a data-driven manner. Performing a DMD analysis requires handling matrices V ∈ R n p × N , where n p and N are indicative of the spatial and temporal resolutions. The DMD analysis of a complex flow field requires long temporal sequences of well resolved data, and thus the memory footprint may become prohibitively large. In this contribution, the effect that principled spatial agglomeration (i.e., reduction in n p via clustering) has on the results derived from the DMD analysis is investigated. We compare twelve different clustering algorithms on three testcases, encompassing different flow regimes: a synthetic flow field, a R e D = 60 flow around a cylinder cross section, and a R... [more]

The latest earthquakes have proven that several existing buildings, particularly in developing countries, are not secured from damages of earthquake. A variety of statistical and machine-learning approaches have been proposed to identify vulnerable buildings for the prioritization of retrofitting. The present work aims to investigate earthquake susceptibility through the combination of six building performance variables that can be used to obtain an optimal prediction of the damage state of reinforced concrete buildings using artificial neural network (ANN). In this regard, a multi-layer perceptron network is trained and optimized using a database of 484 damaged buildings from the Düzce earthquake in Turkey. The results demonstrate the feasibility and effectiveness of the selected ANN approach to classify concrete structural damage that can be used as a preliminary assessment technique to identify vulnerable buildings in disaster risk-management programs.

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 add... [more]

Vehicle-to-grid services draw power or curtail demand from electric vehicles when they are connected to a compatible charging station. In this paper, we investigated automated machine learning for predicting when vehicles are likely to make such a connection. Using historical data collected from a vehicle tracking service, we assessed the technique’s ability to learn and predict when a fleet of 48 vehicles was parked close to charging stations and compared this with two moving average techniques. We found the ability of all three approaches to predict when individual vehicles could potentially connect to charging stations to be comparable, resulting in the same set of 30 vehicles identified as good candidates to participate in a vehicle-to-grid service. We concluded that this was due to the relatively small feature set and that machine learning techniques were likely to outperform averaging techniques for more complex feature sets. We also explored the ability of the approaches to pred... [more]

Weather variables are an important driver of power generation from renewable energy sources. However, accurately predicting such variables is a challenging task, which has a significant impact on the accuracy of the power generation forecasts. In this study, we explore the impact of imperfect weather forecasts on two classes of forecasting methods (statistical and machine learning) for the case of wind power generation. We perform a stress test analysis to measure the robustness of different methods on the imperfect weather input, focusing on both the point forecasts and the 95% prediction intervals. The results indicate that different methods should be considered according to the uncertainty characterizing the weather forecasts.

With the global increase in demand for energy, energy conservation of research and development buildings has become of primary importance for building owners. Knowledge based on the patterns in energy consumption of previous years could be used to predict the near-future energy usage of buildings, to optimize and facilitate more effective energy consumption. Hence, this research aimed to develop a generic model for predicting energy consumption. Air-conditioning was used to exemplify the generic model for electricity consumption, as it is the process that often consumes the most energy in a public building. The purpose of this paper is to present this model and the related findings. After causative factors were determined, the methods of linear regression and various machine learning techniques—including the earlier machine learning techniques of support vector machine, random forest, and multilayer perceptron, and the later machine learning techniques of deep neural network, recurrent... [more]

Demand response (DR) is an integral component of smart grid operations that offers the necessary flexibility to support its decarbonisation. In incentive-based DR programs, deviations from the scheduled DR capacity affect the grid’s energy balance and result in revenue losses for the DR participants. This issue aggravates with increasing DR delivery from participants such as large consumer buildings who have limited standard methods to follow for DR capacity scheduling. Load curtailment based DR capacity availability from such consumers can be forecasted reliably with the help of supervised machine learning (ML) models. This study demonstrates the development of data-driven ML based total and flexible load forecast models for a retail building. The ML model development tasks such as data pre-processing, training-testing dataset preparation, cross-validation, algorithm selection, hyperparameter optimisation, feature ranking, model selection and model evaluation are guided by deployment-... [more]

Wind energy is the most used renewable energy worldwide second only to hydropower. However, the stochastic nature of wind speed makes it harder for wind farms to manage the future power production and maintenance schedules efficiently. Many wind speed prediction models exist that focus on advance neural networks and/or preprocessing techniques to improve the accuracy. Since most of these models require a large amount of historic wind data and are validated using the data split method, the application to real-world scenarios cannot be determined. In this paper, we present a multi-step univariate prediction model for wind speed data inspired by the residual U-net architecture of the convolutional neural network (CNN). We propose a residual dilated causal convolutional neural network (Res-DCCNN) with nonlinear attention for multi-step-ahead wind speed forecasting. Our model can outperform long-term short-term memory networks (LSTM), gated recurrent units (GRU), and Res-DCCNN using sliding... [more]

This paper uses a two-layered soft voting-based ensemble model to predict the interfacial tension (IFT), as one of the transformer oil test parameters. The input feature vector is composed of acidity, water content, dissipation factor, color and breakdown voltage. To test the generalization of the model, the training data was obtained from one utility company and the testing data was obtained from another utility. The model results in an optimal accuracy of 0.87 and a F1-score of 0.89. Detailed studies were also carried out to find the conditions under which the model renders optimal results.

Prediction models in mobility and transportation maintenance systems have been dramatically improved by using machine learning methods. This paper proposes novel machine learning models for an intelligent road inspection. The traditional road inspection systems based on the pavement condition index (PCI) are often associated with the critical safety, energy and cost issues. Alternatively, the proposed models utilize surface deflection data from falling weight deflectometer (FWD) tests to predict the PCI. Machine learning methods are the single multi-layer perceptron (MLP) and radial basis function (RBF) neural networks as well as their hybrids, i.e., Levenberg−Marquardt (MLP-LM), scaled conjugate gradient (MLP-SCG), imperialist competitive (RBF-ICA), and genetic algorithms (RBF-GA). Furthermore, the committee machine intelligent systems (CMIS) method was adopted to combine the results and improve the accuracy of the modeling. The results of the analysis have been verified through using... [more]

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 predict... [more]

Environmental and economic needs drive the increased penetration of intermittent renewable energy in electricity grids, enhancing uncertainty in the prediction of market conditions and network constraints. Thereafter, the importance of energy systems with flexible dispatch is reinforced, ensuring energy storage as an essential asset for these systems to be able to balance production and demand. In order to do so, such systems should participate in wholesale energy markets, enabling competition among all players, including conventional power plants. Consequently, an effective dispatch schedule considering market and resource uncertainties is crucial. In this context, an innovative dispatch optimization strategy for schedule planning of renewable systems with storage is presented. Based on an optimization algorithm combined with a machine-learning approach, the proposed method develops a financial optimal schedule with the incorporation of uncertainty information. Simulations performed w... [more]

As the world is aware, the trend of generating energy sources has been changing from conventional fossil fuels to sustainable energy. In order to reduce greenhouse gas emissions, the ratio of renewable energy sources should be increased, and solar and wind power, typically, are driving this energy change. However, renewable energy sources highly depend on weather conditions and have intermittent generation characteristics, thus embedding uncertainty and variability. As a result, it can cause variability and uncertainty in the power system, and accurate prediction of renewable energy output is essential to address this. To solve this issue, much research has studied prediction models, and machine learning is one of the typical methods. In this paper, we used a bagging model to predict solar energy output. Bagging generally uses a decision tree as a base learner. However, to improve forecasting accuracy, we proposed a bagging model using an ensemble model as a base learner and adding pas... [more]

The prediction of electricity generation is one of the most important tasks in the management of modern energy systems. Improving the assertiveness of this prediction can support government agencies, electric companies, and power suppliers in minimizing the electricity cost to the end consumer. In this study, the problem of forecasting the energy demand in the Brazilian Interconnected Power Grid was addressed, by gathering different energy-related datasets taken from public Brazilian agencies into a unified and open database, used to tune three machine learning models. In contrast to several works in the Brazilian context, which provide only annual/monthly load estimations, the learning approaches Random Forest, Gradient Boosting, and Support Vector Machines were trained and optimized as new ensemble-based predictors with parameter tuning to reach accurate daily/monthly forecasts. Moreover, a detailed and in-depth exploration of energy-related data as obtained from the Brazilian power... [more]

Rotating machines are pivotal to the achievement of core operational objectives within various industries. Recent drives for developing smart systems coupled with the significant advancements in computational technologies have immensely increased the complexity of this group of critical physical industrial assets (PIAs). Vibration-based techniques have contributed significantly towards understanding the failure modes of rotating machines and their associated components. However, the very large data requirements attributable to routine vibration-based fault diagnosis at multiple measurement locations has led to the quest for alternative approaches that possess the capability to reduce faults diagnosis downtime. Initiatives aimed at rationalising vibration-based condition monitoring data in order to just retain information that offer maximum variability includes the combination of coherent composite spectrum (CCS) and principal components analysis (PCA) for rotor-related faults diagnosis... [more]

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 an... [more]

The main purpose of this paper is to present a novel algorithmic reinforcement learning (RL) method for damping the voltage and frequency oscillations in a micro-grid (MG) with penetration of wind turbine generators (WTG). First, the continuous-time environment of the system is discretized to a definite number of states to form the Markov decision process (MDP). To solve the modeled discrete RL-based problem, Q-learning method, which is a model-free and simple iterative solution mechanism is used. Therefore, the presented control strategy is adaptive and it is suitable for the realistic power systems with high nonlinearities. The proposed adaptive RL controller has a supervisory nature that can improve the performance of any kind of controllers by adding an offset signal to the output control signal of them. Here, a part of Denmark distribution system is considered and the dynamic performance of the suggested control mechanism is evaluated and compared with fuzzy-proportional integral... [more]

Tourism development in ecologically vulnerable areas like the lake Baikal region in Eastern Siberia is a challenging problem. To this end, the dynamical models of AC/DC hybrid isolated power system consisting of four power grids with renewable generation units and energy storage systems are proposed using the advanced methods based on deep reinforcement learning and integral equations. First, the wind and solar irradiance potential of several sites on the lake Baikal’s banks is analyzed as well as the electric load as a function of the climatic conditions. The optimal selection of the energy storage system components is supported in online mode. The approach is justified using the retrospective meteorological datasets. Such a formulation will allow us to develop a number of valuable recommendations related to the optimal control of several autonomous AC/DC hybrid power systems with different structures, equipment composition and kind of AC or DC current. Developed approach provides the... [more]

In most cases, the benchmarking models of energy use in houses are developed based on current and past data, and they continue to be used without any update. This paper proposes the method of retraining of benchmarking models by applying machine learning techniques when new measurements are made available. The method uses as a case study the measurements of heating energy demand from two semi-detached houses of Northern Canada. The results of the prediction of heating energy demand using static or augmented window techniques are compared with measurements. The daily energy signature is used as a benchmarking model due to its simplicity and performance. However, the proposed retraining method can be applied to any form of benchmarking model. The method should be applied in all possible situations, and be an integral part of intelligent building automation and control systems (BACS) for the ongoing commissioning for building energy-related applications.

Operation and maintenance (O&M) costs, and associated uncertainty, for wind turbines (WTs) is a significant burden for wind farm operators. Many wind turbine failures are unpredictable while causing loss of energy production, and may also cause loss of asset. This study utilized 753 O&M event data from 21 wind turbines operating in Germany, to improve the prediction of failure frequency and associated costs. We applied Bayesian updating to predict wind turbine failure frequency and time-to-repair (TTR), in conjunction to machine learning techniques for assessing costs associated with failures. We found that time-to-failure (TTF), time-to-repair and the cost of failures depend on operational and environmental conditions. High elevation (>100 m) of the wind turbine installation was found to increase both the probability of failures and probability of delayed repairs. Furthermore, it was determined that direct-drive turbines are more favorable at locations with high capacity factor (more... [more]

Energy efficiency has aroused great interest in research worldwide, because energy consumption has increased in recent years, especially in the residential sector. The advances in energy conversion, along with new forms of communication, and information technologies have paved the way for what is now known as smart homes. The Internet of Things (IoT) is the convergence of various heterogeneous technologies from different application domains that are used to interconnect things through the Internet, thus allowing for the detection, monitoring, and remote control of multiple devices. Home automation systems (HAS) combined with IoT, big data technologies, and machine learning are alternatives that promise to contribute to greater energy efficiency. This work presents HEMS-IoT, a big data and machine learning-based smart home energy management system for home comfort, safety, and energy saving. We used the J48 machine learning algorithm and Weka API to learn user behaviors and energy consu... [more]