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Growing dependence on fossil fuels is one of the critical factors accelerating climate change, a global concern that can destabilize ecosystems and economies worldwide. In this context, renewable energy is emerging as a sustainable and environmentally responsible alternative. Among the options, geothermal energy stands out for its ability to provide heat and electricity consistently and efficiently, offering a feasible solution to reduce the carbon footprint and promote more sustainable development in a globalized economy. In this work, a machine learning approach is proposed to predict the behavior of a horizontal heat exchanger from a bioclimatic house. First, a correlation analysis was conducted for optimal feature selection. Then, several regression techniques were applied to predict the output temperature of the geothermal exchanger. Satisfactory prediction results were obtained in different scenarios over the whole dataset. Also, a significant correlation between several sensors was concluded. [ABSTRACT FROM AUTHOR]

Energy consumption is an important indicator for measuring economic development and is closely related to the atmospheric environment. As a demonstration zone for China's high-quality development, the Guangdong–Hong Kong–Macao Greater Bay Area imposes higher requirements on ecological environment and sustainable development. Therefore, accurate data on energy consumption is crucial for high-quality green development. However, the statistical data on local energy consumption in China is insufficient, and the lack of data is severe, which hinders the analysis of energy consumption at the metropolitan level and the precise implementation of energy policies. Nighttime light data have been widely used in the inversion of energy consumption, but they can only reflect socio-economic activities at night with certain limitations. In this study, a random forest model was developed to estimate metropolitan-level energy consumption in the Guangdong–Hong Kong–Macao Greater Bay Area from 2000 to 2020 based on nighttime light data, population data, and urban impervious surface data. The estimation results show that our model shows good performance with an R2 greater than 0.9783 and MAPE less than 9%. A long time series dataset from 2000 to 2020 on energy consumption distribution at a resolution of 500 m in the Guangdong–Hong Kong–Macao Greater Bay Area was built using our model with a top-down weight allocation method. The spatial and temporal dynamics of energy consumption in the Greater Bay Area were assessed at both the metropolitan and grid levels. The results show a significant increase in energy consumption in the Greater Bay Area with a clear clustering, and approximately 90% of energy consumption is concentrated in 22% of the area. This study established an energy consumption estimation model that comprehensively considers population, urban distribution, and nighttime light data, which effectively solves the problem of missing statistical data and accurately reflects the spatial distribution of energy consumption of the whole Bay Area. This study provides a reference for spatial pattern analysis and refined urban management and energy allocation for regions lacking statistical data on energy consumption. [ABSTRACT FROM AUTHOR]

Energy consumption plays an important role in contemporary economies. Its significance extends beyond utilitarian value, impacting economic robustness, environmental protection, and residents' well-being. The escalating global energy requisites necessitate efficient energy utilization and a shift towards renewable sources to address climate change and strengthen energy independence. Developing accurate predictive models to forecast long-term energy costs and savings remains a complex problem. This paper aims to provide a methodology to identify the influence of building energy performance on real estate market efficiency, focusing on property maintenance costs. Real estate plays a crucial role in human life, serving both as a fundamental need and as a vehicle for achieving personal aspirations and secure financial investments, particularly during times of economic and social instability. Through interdisciplinary methodological architecture, this study addresses three key issues: the impact of rising energy costs on market efficiency, the responsiveness of the real estate market to energy price fluctuations, and the significance of property maintenance costs on market value. The research approach includes creating and applying AI algorithms capable of evaluating extensive datasets pertaining to real estate features. Utilizing machine learning methods, the algorithm determines the importance of energy efficiency measures as well as various other inherent and external attributes of properties. The suggested methodology provides a novel approach to improve the effectiveness of market efficiency analysis. [ABSTRACT FROM AUTHOR]

With the continuous improvement of shale oil and gas recovery technologies and achievements, a large amount of geological information and data have been accumulated for the description of shale reservoirs, and it has become possible to use machine learning methods for "sweet spots" prediction in shale oil and gas areas. Taking the Duvernay shale oil and gas field in Canada as an example, this paper attempts to build recoverable shale oil and gas reserve prediction models using machine learning methods and geological and development big data, to predict the distribution of recoverable shale oil and gas reserves and provide a basis for well location deployment and engineering modifications. The research results of the machine learning model in this study are as follows: ① Three machine learning methods were applied to build a prediction model and random forest showed the best performance. The R2 values of the built recoverable shale oil and gas reserves prediction models are 0.7894 and 0.8210, respectively, with an accuracy that meets the requirements of production applications; ② The geological main controlling factors for recoverable shale oil and gas reserves in this area are organic matter maturity and total organic carbon (TOC), followed by porosity and effective thickness; the main controlling factor for engineering modifications is the total proppant volume, followed by total stages and horizontal lateral length; ③ The abundance of recoverable shale oil and gas reserves in the central part of the study area is predicted to be relatively high, which makes it a favorable area for future well location deployment. [ABSTRACT FROM AUTHOR]

Short-term load forecasting (STLF) plays a vital role in ensuring the safe, efficient, and economical operation of power systems. Accurate load forecasting provides numerous benefits for power suppliers, such as cost reduction, increased reliability, and informed decision-making. However, STLF is a complex task due to various factors, including non-linear trends, multiple seasonality, variable variance, and significant random interruptions in electricity demand time series. To address these challenges, advanced techniques and models are required. This study focuses on the development of an efficient short-term power load forecasting model using the random forest (RF) algorithm. RF combines regression trees through bagging and random subspace techniques to improve prediction accuracy and reduce model variability. The algorithm constructs a forest of trees using bootstrap samples and selects random feature subsets at each node to enhance diversity. Hyperparameters such as the number of trees, minimum sample leaf size, and maximum features for each split are tuned to optimize forecasting results. The proposed model was tested using historical hourly load data from four transformer substations supplying different campus areas of the University of Beira Interior, Portugal. The training data were from January 2018 to December 2021, while the data from 2022 were used for testing. The results demonstrate the effectiveness of the RF model in forecasting short-term hourly and one day ahead load and its potential to enhance decision-making processes in smart grid operations. [ABSTRACT FROM AUTHOR]

Fossil fuel vehicles significantly contribute to CO 2 emissions due to their high consumption of fossil fuels. Accurate estimation of vehicular fuel consumption and the associated CO 2 emissions is crucial for mitigating these emissions. Although driving behavior is a vital factor influencing fuel consumption and CO 2 emissions, it remains largely unaddressed in current CO 2 emission estimation models. This study incorporates novel driving behavior data, specifically counts of occurrences of dangerous driving behaviors, including speeding, sudden accelerating, and sudden braking, as well as driving time and driving distances on expressways, national highways, and local roads, respectively, into monthly fuel consumption estimation models for individual gasoline and hybrid vehicles. The CO 2 emissions are then calculated as a secondary parameter based on the estimated fuel consumption, assuming a linear relationship between the two. Using regression algorithms, it has been demonstrated that all the proposed driving behavior data are relevant for monthly CO 2 emission estimation. By integrating the driving behavior data of various vehicle categories, a generalizable CO 2 estimation model is proposed. When utilizing all the proposed driving behavior data collectively, our random forest regression model achieves the highest prediction accuracy, with R 2 , RMSE, and MAE values of 0.975, 13.293 kg, and 8.329 kg, respectively, for monthly CO 2 emission estimation of individual vehicles. This research offers insights into CO 2 emission reduction and energy conservation in the road transportation sector. [ABSTRACT FROM AUTHOR]

This study aims to forecast New York and Los Angeles gasoline spot prices on a daily frequency. The dataset includes gasoline prices and a big set of 128 other relevant variables spanning the period from 17 February 2004 to 26 March 2022. These variables were fed to three tree-based machine learning algorithms: decision trees, random forest, and XGBoost. Furthermore, a variable importance measure (VIM) technique was applied to identify and rank the most important explanatory variables. The optimal model, a trained random forest, achieves a mean absolute percent error (MAPE) in the out-of-sample of 3.23% for the New York and 3.78% for the Los Angeles gasoline spot prices. The first lag, AR (1), of gasoline is the most important variable in both markets; the top five variables are all energy-related. This paper can strengthen the understanding of price determinants and has the potential to inform strategic decisions and policy directions within the energy sector, making it a valuable asset for both industry practitioners and policymakers. [ABSTRACT FROM AUTHOR]

Dynamic economic emission dispatch problems are complex optimization tasks in power systems that aim to simultaneously minimize both fuel costs and pollutant emissions while satisfying various system constraints. Traditional methods often involve solving intricate nonlinear load flow equations or employing approximate loss formulas to account for transmission losses. These methods can be computationally expensive and may not accurately represent the actual transmission losses, affecting the overall optimization results. To address these limitations, this study proposes a novel approach that integrates transmission loss prediction into the dynamic economic emission dispatch (DEED) problem. A Random Forest machine learning model was offline-trained to predict transmission losses accurately, eliminating the need for repeated calculations during each iteration of the optimization process. This significantly reduced the computational burden of the algorithm and improved its efficiency. The proposed method utilizes a powerful multi-objective stochastic paint optimizer to solve the highly constrained and complex dynamic economic emission dispatch problem integrated with random forest-based loss prediction. A fuzzy membership-based approach was employed to determine the best compromise Pareto-optimal solution. The proposed algorithm integrated with loss prediction was validated on widely used five and ten-unit power systems with B-loss coefficients. The results obtained using the proposed algorithm were compared with seventeen algorithms available in the literature, demonstrating that the multi-objective stochastic paint optimizer (MOSPO) outperforms most existing algorithms. Notably, for the Institute of Electrical and Electronics Engineers (IEEE) thirty bus system, the proposed algorithm achieves yearly fuel cost savings of USD 37,339.5 and USD 3423.7 compared to the existing group search optimizer algorithm with multiple producers (GSOMP) and multi-objective multi-verse optimization (MOMVO) algorithms. [ABSTRACT FROM AUTHOR]

Road vehicles, particularly cars, are one of the primary sources of CO2 emissions in the transport sector. Shifting to unconventional energy sources such as solar and wind power may reduce their carbon footprints considerably. Consequently, using ammonia as a fuel due to its potential benefits, such as its high energy density, being a carbon-free fuel, and its versatility during storage and transportation, has now grabbed the attention of researchers. However, its slow combustion speed, larger combustion chamber requirements, ignition difficulties, and limited combustion stability are still major challenges. Therefore, authors tried to analyze the combustion pressure of ammonia in a constant-volume combustion chamber across different equivalence ratios by adopting a machine learning approach. While conducting the analysis, the experimental values were assessed and subsequently utilized to predict the induced combustion pressure in a constant-volume combustion chamber across various equivalence ratios. In this research, a two-step prediction process was employed. In the initial step, the Random Forest algorithm was applied to assess the combustion pressure. Subsequently, in the second step, artificial neural network machine learning algorithms were employed to pinpoint the most effective algorithm with a lower root-mean-square error and R2. Finally, Linear Regression illustrated the lowest error in both steps with a value of 1.0, followed by Random Forest. [ABSTRACT FROM AUTHOR]

CO2 flooding is a pivotal technique for significantly enhancing oil recovery in low-permeability reservoirs. The movement and sweeping rules at the front of CO2 flooding play a critical role in oil recovery; yet, a comprehensive quantitative analysis remains an area in need of refinement. In this study, we developed 1-D and 2-D numerical simulation models to explore the sweeping behavior of miscible, immiscible, and partly miscible CO2 flooding patterns. The front position and movement rules of the three CO2 flooding patterns were determined. A novel approach to the contour area calculation method was introduced to quantitatively characterize the sweep coefficients, and the sweeping rules are discussed regarding the geological parameters, oil viscosity, and injection–production parameters. Furthermore, the Random Forest (RF) algorithm was employed to identify the controlling factor of the sweep coefficient, as determined through the use of out-of-bag (OOB) data permutation analysis. The results showed that the miscible front was located at the point of maximum CO2 content in the oil phase. The immiscible front occurred at the point of maximum interfacial tension near the production well. Remarkably, the immiscible front moved at a faster rate compared with the miscible front. Geological parameters, including porosity, permeability, and reservoir thickness, significantly impacted the gravity segregation effect, thereby influencing the CO2 sweep coefficient. Immiscible flooding exhibited the highest degree of gravity segregation, with a maximum gravity segregation degree (GSD) reaching 78.1. The permeability ratio was a crucial factor, with a lower limit of approximately 5.0 for reservoirs suitable for CO2 flooding. Injection–production parameters also played a pivotal role in terms of the sweep coefficient. Decreased well spacing and increased gas injection rates were found to enhance sweep coefficients by suppressing gravity segregation. Additionally, higher gas injection rates could improve the miscibility degree of partly miscible flooding from 0.69 to 1.0. Oil viscosity proved to be a significant factor influencing the sweep coefficients, with high seepage resistance due to increasing oil viscosity dominating the miscible and partly miscible flooding patterns. Conversely, gravity segregation primarily governed the sweep coefficient in immiscible flooding. In terms of controlling factors, the permeability ratio emerged as a paramount influence, with a factor importance value (FI) reaching 1.04. The findings of this study can help for a better understanding of sweeping rules of CO2 flooding and providing valuable insights for optimizing oil recovery strategies in the field applications of CO2 flooding. [ABSTRACT FROM AUTHOR]

In recent years, with the development of societies and economies, the demand for social electricity has further increased. The efficiency and accuracy of electric-load forecasting is an important guarantee for the safety and reliability of power system operation. With the sparrow search algorithm (SSA), long short-term memory (LSTM), and random forest (RF), this research proposes an SSA-LSTM-RF daily peak-valley forecasting model. First, this research uses the Pearson correlation coefficient and the random forest model to select features. Second, the forecasting model takes the target value, climate characteristics, time series characteristics, and historical trend characteristics as input to the LSTM network to obtain the daily-load peak and valley values. Third, the super parameters of the LSTM network are optimized by the SSA algorithm and the global optimal solution is obtained. Finally, the forecasted peak and valley values are input into the random forest as features to obtain the output of the peak-valley time. The forest value of the SSA-LSTM-RF model is good, and the fitting ability is also good. Through experimental comparison, it can be seen that the electric-load forecasting algorithm based on the SSA-LSTM-RF model has higher forecasting accuracy and provides ideal performance for electric-load forecasting with different time steps. [ABSTRACT FROM AUTHOR]

Compressional velocity (Vp) and bulk density (ρb) logs are essential for characterizing gas hydrates and near-seafloor sediments; however, it is sometimes difficult to acquire these logs due to poor borehole conditions, safety concerns, or cost-related issues. We present a machine learning approach to predict either compressional Vp or ρb logs with high accuracy and low error in near-seafloor sediments within water-saturated intervals, in intervals where hydrate fills fractures, and intervals where hydrate occupies the primary pore space. We use scientific-quality logging-while-drilling well logs, gamma ray, ρb, Vp, and resistivity to train the machine learning model to predict Vp or ρb logs. Of the six machine learning algorithms tested (multilinear regression, polynomial regression, polynomial regression with ridge regularization, K nearest neighbors, random forest, and multilayer perceptron), we find that the random forest and K nearest neighbors algorithms are best suited to predicting Vp and ρb logs based on coefficients of determination (R2) greater than 70% and mean absolute percentage errors less than 4%. Given the high accuracy and low error results for Vp and ρb prediction in both hydrate and water-saturated sediments, we argue that our model can be applied in most LWD wells to predict Vp or ρb logs in near-seafloor siliciclastic sediments on continental slopes irrespective of the presence or absence of gas hydrate. [ABSTRACT FROM AUTHOR]

Nearly 60% of commercial customers are connected to a low-voltage network in Poland with a contractual capacity of more than 40 kW and are assigned a fixed tariff with flat prices for the whole year, no matter the usage volume. With smart meters, more data about how businesses use energy are becoming available to both energy providers and customers. This enables innovation in the structure and type of tariffs on offer in the energy market. Customers can explore their usage patterns to choose the most suitable tariff to benefit from lower prices and thus generate savings. In this paper, we analyzed whether customers' electricity usage matched their optimal tariff and further investigated which of them could benefit or lose from switching the tariff based on the real dataset with the hourly energy readings of 1212 commercial entities in Poland recorded between 2016 and 2019. Three modelling approaches, i.e., the k-nearest neighbors, classification tree and random forest, were tested for optimal tariff classification, while for the benchmark, we used a simple approach, in which the tariff was proposed based on the customers' previous electricity usage. The main findings from the research are threefold: (1) out of all the analyzed entities, on average, 76% of them could have benefited from the tariff switching, which suggests that customers may not be aware of the tariff change benefits, or they had chosen a tariff plan that was not tailored to them; (2) a random forest model offers a viable approach to accurate tariff classification; (3) the policy implication from the research is the need to increase the customers' awareness about the tariffs and propose reliable tools for selecting the optimal tariff. [ABSTRACT FROM AUTHOR]

In this paper, we address several issues with existing methods for predicting the residual electrical life of railway relays. These issues include the difficulty of single feature prediction in characterizing the degradation process, the neglect of temporal and backward–forward correlations in the degradation process, and low prediction accuracy. To overcome these challenges, we propose a novel approach that combines convolutional neural networks (CNNs) and bidirectional long short-term memory (BiLSTM) to facilitate the life prediction of railway relays and provide an accurate data basis for the maintenance of railway relays. Firstly, we collected voltage and current signals from railway relay electrical life tests and extracted feature parameters that captured the relay's operating state. Next, we applied Spearman correlation coefficient analysis combined with random forest importance analysis to perform double-feature selection. This process eliminates redundant feature parameters and identifies the optimal feature subset. Finally, we constructed a convolutional neural network bidirectional long short-term memory (CNN BiLSTM) prediction model to accurately predict the remaining electrical life of the railway relay. Through our analysis of the prediction results, we observed that the CNN BiLSTM model achieves an effective prediction accuracy of 96.3%. This accuracy is significantly higher, more stable, and more practical compared to other prediction models such as recurrent neural networks (RNNs), long short-term memory (LSTM), and BiLSTM models. Overall, our proposed CNN BiLSTM model offers higher accuracy, better stability, and greater practicality in predicting the remaining electrical life of railway relays. [ABSTRACT FROM AUTHOR]

Complex oil and gas two-phase flow exists within an aero-engines bearing cavity scavenge pipe, prone to lubricated self-ignition and coking. Lubricant system designers must be able to accurately identify and understand the flow state of the scavenge pipe. The prediction accuracy of previous models is insufficient to meet the more demanding needs. This paper establishes a visualized flow pattern identification test system for the scavenge pipe, with a test temperature of up to 370 k, using a high-speed camera to photograph four flow patterns, decomposing the pressure signals obtained from high-frequency dynamic pressure sensors using the ensemble empirical mode decomposition (EEMD) method, and then performing Hilbert transform, using the Hilbert spectrum to quantify the changes of amplitude and frequency with time, and establishing the energy and flow pattern correspondence analysis. Then the energy percentage of IMFs is used as the input of feature values, and the random forest algorithm machine learning is used for predictive classification. The experimental results show that the flow pattern recognition rate established in this paper can reach 98%, which can identify the two-phase flow pattern in the scavenge pipe more objectively and accurately. [ABSTRACT FROM AUTHOR]

The complication of the structure, topology and composition of the future electrical networks is characterized by difficult-to-recognize circuit-mode situations and requires modern methods for analyzing information parameters. The growing trend of digitizing signals in substations and the use of the IEC 61850 standard results in a huge amount of new data available at the nodes of the electrical network. The development and analysis of new methods for detecting and recognizing the modes of electrical networks (normal and emergency) are topical research issues. The article explores a new approach to recognizing a faulted section of an electrical network with branches by concurrently analyzing several information features and applying machine learning methods: decision tree, random forest, and gradient boosting. The application of this approach for decision-making by relay protection has not been previously implemented. Simulation modeling and the Monte Carlo method are at the heart of obtaining training samples. The results of testing the studied methods under review showed the required flexibility, the ability to use a large number of information parameters, as well as the best results of fault recognition in comparison with the distance protection relay. [ABSTRACT FROM AUTHOR]

The stator winding insulation system is the most critical and weakest part of the EMU's (electric multiple unit's) traction motor. The effective diagnosis for stator ITSC (inter-turn short-circuit) faults can prevent a fault from expanding into phase-to-phase or ground short-circuits. The TCU (traction control unit) controls the traction inverter to output SPWM (sine pulse width modulation) excitation voltage when the traction motor is at a standstill. Three ITSC fault diagnostic conditions are based on different IGBTs' control logics. The Goertzel algorithm is used to calculate the fundamental current amplitude difference Δi and phase angle difference Δθ of equivalent parallel windings under the three diagnostic conditions. The six parameters under the three diagnostic conditions are used as features to establish an ITSC fault diagnostic model based on the random forest. The proposed method was validated using a simulation experimental platform for the ITSC fault diagnosis of EMU traction motors. The experimental results indicate that the current amplitude features Δi and phase angle features Δθ change obviously with an increase in the ITSC fault extent if the ITSC fault occurs at the equivalent parallel windings. The accuracy of the ITSC fault diagnosis model based on the random forest for ITSC fault detection and location, both in train and test samples, is 100%. [ABSTRACT FROM AUTHOR]

Non-invasive load monitoring (NILM) represents a crucial technology in enabling smart electricity consumption. In response to the challenges posed by high feature redundancy, low identification accuracy, and the high computational costs associated with current load identification models, a novel load identification model based on kernel principal component analysis (KPCA) and random forest (RF) optimized by improved Grey Wolf Optimizer (IGWO) is proposed. Initially, 17 steady-state load characteristics were selected as discrimination indexes. KPCA was subsequently employed to reduce the dimension of the original data and diminish the correlation between the feature indicators. Then, the dimension reduction in load data was classified by RF. In order to improve the performance of the classifier, IGWO was used to optimize the parameters of the RF classifier. Finally, the proposed model was implemented to identify 25 load states consisting of seven devices. The experimental results demonstrate that the identification accuracy of this method is up to 96.8% and the Kappa coefficient is 0.9667. [ABSTRACT FROM AUTHOR]

Participants in deregulated electricity markets face risks from price volatility due to various factors, including fuel prices, renewable energy production, electricity demand, and crises such as COVID-19 and energy-related issues. Price forecasting is used to mitigate risk in markets trading goods which have high price volatility. Forecasting in electricity markets is difficult and challenging as volatility is attributed to many unpredictable factors. This work studies and reports the performance both in terms of forecasting error and of computational time of forecasting algorithms that are based on Extreme Learning Machine, Artificial Neural Network, XGBoost and random forest. All these machine learning techniques are combined with the Bootstrap technique of creating new samples from the available ones in order to improve the forecasting errors. In order to assess the performance of these methodologies, the Day-Ahead market prices are divided into three classes, namely normal, extremely high and negative, and these algorithms are subsequently used to provide forecasts for the whole year 2020 of the German and Finnish Day-Ahead markets. The average yearly forecasting errors along with the computation time required by each methodology are reported. The findings indicate that the random forest algorithm performs best for the normal and extremely high price categories, while XGBoost demonstrates better results for the negative price category. The methodology based on Extreme Learning Machine requires the least computational time and achieves forecasting errors that are comparable to the best-performing methods. [ABSTRACT FROM AUTHOR]

The total organic carbon (TOC) content of organic-rich shale is a key parameter in screening for potential source rocks and sweet spots of shale oil/gas. Traditional methods of determining the TOC content, such as the geochemical experiments and the empirical mathematical regression method, are either high cost and low-efficiency, or universally non-applicable and low-accuracy. In this study, we propose three machine learning models of random forest (RF), support vector regression (SVR), and XGBoost to predict the TOC content using well logs, and the performance of each model are compared with the traditional empirical methods. First, the decision tree algorithm is used to identify the optimal set of well logs from a total of 15. Then, 816 data points of well logs and the TOC content data collected from five different shale formations are used to train and test these three models. Finally, the accuracy of three models is validated by predicting the unknown TOC content data from a shale oil well. The results show that the RF model provides the best prediction for the TOC content, with R2 = 0.915, MSE = 0.108, and MAE = 0.252, followed by the XGBoost, while the SVR gives the lowest predictive accuracy. Nevertheless, all three machine learning models outperform the traditional empirical methods such as Schmoker gamma-ray log method, multiple linear regression method and ΔlgR method. Overall, the proposed machine learning models are powerful tools for predicting the TOC content of shale and improving the oil/gas exploration efficiency in a different formation or a different basin. [ABSTRACT FROM AUTHOR]

Electricity price forecasting (EPF) has become an essential part of decision-making for energy companies to participate in power markets. As the energy mix becomes more uncertain and stochastic, this process has also become important for industrial companies, as their production schedules are greatly impacted by energy costs. Although various approaches have been tested with varying degrees of success, this study focuses on predicting day-ahead market (DAM) prices in different European markets and how this directly affects the optimal production scheduling for various industrial loads. We propose a fuzzy-based architecture that incorporates the results of two forecasting algorithms; a random forest (RF) and a long short-term memory (LSTM). To enhance the accuracy of the proposed model for a specific country, electricity market data from neighboring countries are also included. The developed DAM price forecaster can then be utilized by energy-intensive industries to optimize their production processes to reduce energy costs and improve energy-efficiency. Specifically, the tool is important for industries with multi-site production facilities in neighboring countries, which could reschedule the production processes depending on the forecasted electricity market price. [ABSTRACT FROM AUTHOR]

Short-term load forecasting (STLF) is critical for the energy industry. Accurate predictions of future electricity demand are necessary to ensure power systems' reliable and efficient operation. Various STLF models have been proposed in recent years, each with strengths and weaknesses. This paper comprehensively reviews some STLF models, including time series, artificial neural networks (ANNs), regression-based, and hybrid models. It first introduces the fundamental concepts and challenges of STLF, then discusses each model class's main features and assumptions. The paper compares the models in terms of their accuracy, robustness, computational efficiency, scalability, and adaptability and identifies each approach's advantages and limitations. Although this study suggests that ANNs and hybrid models may be the most promising ways to achieve accurate and reliable STLF, additional research is required to handle multiple input features, manage massive data sets, and adjust to shifting energy conditions. [ABSTRACT FROM AUTHOR]

Hydropower is the most prevalent source of renewable energy production worldwide. As the global demand for robust and ecologically sustainable energy production increases, developing and enhancing the current energy production processes is essential. In the past decade, machine learning has contributed significantly to various fields, and hydropower is no exception. All three horizons of hydropower models could benefit from machine learning: short-term, medium-term, and long-term. Currently, dynamic programming is used in the majority of hydropower scheduling models. In this paper, we review the present state of the hydropower scheduling problem as well as the development of machine learning as a type of optimization problem and prediction tool. To the best of our knowledge, this is the first survey article that provides a comprehensive overview of machine learning and artificial intelligence applications in the hydroelectric power industry for scheduling, optimization, and prediction. [ABSTRACT FROM AUTHOR]

Lithofacies identification and classification are critical for characterizing the hydrocarbon potential of unconventional resources. Although extensive applications of machine learning models in predicting lithofacies have been applied to conventional reservoir systems, the effectiveness of machine learning models in predicting clay-rich, lacustrine shale lithofacies has yet to be tackled. Here, we apply machine learning models to conventional well log data to automatically identify the shale lithofacies of Gulong Shale in the Songliao Basin. The shale lithofacies were classified into six types based on total organic carbon and mineral composition data from core analysis and geochemical logs. We compared the accuracy of Multilayer Perceptron (MLP), Support Vector Machine (SVM), Extreme Gradient Boosting (XGBoost), and Random Forest models. We mitigated the bias of imbalanced data by applying oversampling algorithms. Our results show that ensemble methods (XGBoost and Random Forest) have a better performance in shale lithofacies identification than the other models do, with accuracies of 0.868 and 0.884, respectively. The organic siliceous shale proposed to have the best hydrocarbon potential in Gulong Shale can be identified with F1 scores of 0.853 by XGBoost and 0.877 by Random Forest. Our study suggests that ensemble machine learning models can effectively identify the lithofacies of clay-rich shale from conventional well logs, providing insight into the sweet spot prediction of unconventional reservoirs. Further improvements in model performances can be achieved by adding domain knowledge and employing advanced well log data. [ABSTRACT FROM AUTHOR]

Porous carbons as solid adsorbent materials possess effective porosity characteristics that are the most important factors for gas storage. The chemical activating routes facilitate hydrogen storage by adsorbing on the high surface area and microporous features of porous carbon-based adsorbents. The present research proposed to predict H2 storage using four nature-inspired algorithms applied in the random forest (RF) model. Various carbon-based adsorbents, chemical activating agents, ratios, micro-structural features, and operational parameters as input variables are applied in the ML model to predict H2 uptake (wt%). Particle swarm and gray wolf optimizations (PSO and GWO) in the RF model display accuracy in the train and test phases, with an R2 of ~0.98 and 0.91, respectively. Sensitivity analysis demonstrated the ranks for temperature, total pore volume, specific surface area, and micropore volume in first to fourth, with relevancy scores of 1 and 0.48. The feasibility of algorithms in training sizes 80 to 60% evaluated that RMSE and MAE achieved 0.6 to 1, and 0.38 to 0.52. This study contributes to the development of sustainable energy sources by providing a predictive model and insights into the design of porous carbon adsorbents for hydrogen storage. The use of nature-inspired algorithms in the model development process is also a novel approach that could be applied to other areas of materials science and engineering. [ABSTRACT FROM AUTHOR]

Challenges in utilising fossil fuels for generating energy call for the adoption of renewable energy sources. This study focuses on modelling and nowcasting optimal tilt angle(s) of solar energy harnessing using historical time series data collected from one of South Africa's radiometric stations, USAid Venda station in Limpopo Province. In the study, we compared random forest (RF), K-nearest neighbours (KNN), and long short-term memory (LSTM) in nowcasting of optimum tilt angle. Gradient boosting (GB) is used as the benchmark model to compare the model's predictive accuracy. The performance measures of mean absolute error (MAE), mean square error (MSE), root mean square error (RMSE) and R 2 were used, and the results showed LSTM to have the best performance in nowcasting optimum tilt angle compared to other models, followed by the RF and GB, whereas KNN was the worst-performing model. [ABSTRACT FROM AUTHOR]

Reservoir simulation is a time-consuming procedure that requires a deep understanding of complex fluid flow processes as well as the numerical solution of nonlinear partial differential equations. Machine learning algorithms have made significant progress in modeling flow problems in reservoir engineering. This study employs machine learning methods such as random forest, decision trees, gradient boosting regression, and artificial neural networks to forecast nanoparticle transport with the two-phase flow in porous media. Due to the shortage of data on nanoparticle transport in porous media, this work creates artificial datasets using a mathematical model. It predicts nanoparticle transport behavior using machine learning techniques, including gradient boosting regression, decision trees, random forests, and artificial neural networks. Utilizing the scikit-learn toolkit, strategies for data preprocessing, correlation, and feature importance are addressed. Furthermore, the GridSearchCV algorithm is used to optimize hyperparameter tuning. The mean absolute error, R-squared correlation, mean squared error, and root means square error are used to assess the models. The ANN model has the best performance in forecasting the transport of nanoparticles in porous media, according to the results. [ABSTRACT FROM AUTHOR]

In this paper, ensemble-based machine learning models with gradient boosting machine and random forest are proposed for predicting the power production from six different solar PV systems. The models are based on three year's performance of a 1.2 MW grid-integrated solar photo-voltaic (PV) power plant. After cleaning the data for errors and outliers, the model features were chosen on the basis of principal component analysis. Accuracies of the developed models were tested and compared with the performance of models based on other supervised learning algorithms, such as k-nearest neighbour and support vector machines. Though the accuracies of the models varied with the type of PV systems, in general, the machine learned models developed under the study could perform well in predicting the power output from different solar PV technologies under varying working environments. For example, the average root mean square error of the models based on the gradient boosting machines, random forest, k-nearest neighbour, and support vector machines are 17.59 kW, 17.14 kW, 18.74 kW, and 16.91 kW, respectively. Corresponding averages of mean absolute errors are 8.28 kW, 7.88 kW, 14.45 kW, and 6.89 kW. Comparing the different modelling methods, the decision-tree-based ensembled algorithms and support vector machine models outperformed the approach based on the k-nearest neighbour method. With these high accuracies and lower computational costs compared with the deep learning approaches, the proposed ensembled models could be good options for PV performance predictions used in real and near-real-time applications. [ABSTRACT FROM AUTHOR]

Nowadays, electric load forecasting through a data analytic approach has become one of the most active and emerging research areas. It provides future consumption patterns of electric load. Since there are large fluctuations in both electricity production and use, it is a difficult task to achieve a balance between electric load and demand. By analyzing past electric consumption records to estimate the upcoming electricity load, the issue of fluctuating behavior can be resolved. In this study, a framework for feature selection, extraction, and regression is put forward to carry out the electric load prediction. The feature selection phase uses a combination of extreme gradient boosting (XGB) and random forest (RF) to determine the significance of each feature. Redundant features in the feature extraction approach are removed by applying recursive feature elimination (RFE). We propose an enhanced support vector machine (ESVM) and an enhanced convolutional neural network (ECNN) for the regression component. Hyperparameters of both the proposed approaches are set using the random search (RS) technique. To illustrate the effectiveness of our proposed strategies, a comparison is also performed between the state-of-the-art approaches and our proposed techniques. In addition, we perform statistical analyses to prove the significance of our proposed approaches. Simulation findings illustrate that our proposed approaches ECNN and ESVM achieve higher accuracies of 98.83% and 98.7%, respectively. [ABSTRACT FROM AUTHOR]

Solar radiation is by nature intermittent and influenced by many factors such as latitude, season and atmospheric conditions. As a consequence, the growing penetration of Photovoltaic (PV) systems into the electricity network implies significant problems of stability, reliability and scheduling of power grid operation. Concerning the very short-term PV power production, the power fluctuations are primarily related to the interaction between solar irradiance and cloud cover. In small-scale systems such as microgrids, the adoption of a forecasting tool is a brilliant solution to minimize PV power curtailment and limit the installed energy storage capacity. In the present work, two different nowcasting methods are applied to classify the solar attenuation due to clouds presence on five different forecast horizons, from 1 to 5 min: a Pattern Recognition Neural Network and a Random Forest model. The proposed methods are tested and compared on a real case study: available data consists of historical irradiance measurements and infrared sky images collected in a real PV facility, the SolarTechLAB in Politecnico di Milano. The classification output is a range of values corresponding to the future value assumed by the Clear Sky Index (CSI), an indicator allowing to account for irradiance variations only related to clouds passage, neglecting diurnal and seasonal influences. The developed models present similar performance in all the considered time horizons, reliably detecting the CSI drops caused by incoming overcast and partially cloudy sky conditions. [ABSTRACT FROM AUTHOR]

Due to complexities in geologic structure, heterogeneity, and insufficient borehole information, shale formation faces challenges in accurately estimating the elastic properties of rock which triggers severe technical challenges in safe drilling and completion. These geomechanical properties could be computed from acoustic logs, however, accurate estimation is critical due to log deficit and a higher recovery expense of inadequate datasets. To fill the gap, this study focuses on predicting the sonic properties of rock using deep neural network (Bi-directional long short-time memory, Bi-LSTM) and random forest (RF) algorithms to estimate and evaluate the geomechanical properties of the potential unconventional formation, Permian Basin, situated in West Texas. A total of three wells were examined using both single-well and cross-well prediction algorithms. Log-derived single-well prediction models include a 75:25 ratio for training and testing the data whereas the cross-well includes two wells for training and the remaining well was used for testing. The selected well input logs include compressional wave slowness, resistivity, gamma-ray, porosity, and bulk density to predict shear wave slowness. The results using RF and Bi-LSTM show a promising prediction of geomechanical properties for Permian Basin wells. RF algorithm performed superior for both single and grouped well prediction. The single-well prediction method using the RF algorithm provided the highest accuracy of 99.90% whereas Bi-LSTM gave 93.60%. The best accuracy for a grouped well prediction was achieved employing Bi-LSTM and RF models, i.e., 96.01% and 93.80%. The average prediction including RF and Bi-LSTM algorithms demonstrated that accuracy for single well and cross well prediction is 96% and 94% respectively with an error below 7%. These outcomes show the astonishing capability of artificial intelligence (AI) models trained to create a realistic prediction to unlock unconventional potential when datasets are inadequate. Given adequate training data, operators could leverage these efficient tools by utilizing them to examine fracture interpretations with reduced cost and time when datasets are incomplete and thus increase the hydrocarbon recovery potential. [ABSTRACT FROM AUTHOR]

Reliable energy consumption forecasting is essential for building energy efficiency improvement. Regression models are simple and effective for data analysis, but their practical applications are limited by the low prediction accuracy under ever-changing building operation conditions. To address this challenge, a Joinpoint–Multiple Linear Regression (JP–MLR) model is proposed in this study, based on the investigation of the daily electricity usage data of 8 apartment complexes located within a university in Xiamen, China. The univariate model is first built using the Joinpoint Regression (JPR) method, and then the remaining residuals are evaluated using the Multiple Linear Regression (MLR) method. The model contains six explanatory variables, three of which are continuous (mean outdoor air temperature, mean relative humidity, and temperature amplitude) and three of which are categorical (gender, holiday index, and sunny day index). The performance of the JP–MLR model is compared to that of the other four data-driven algorithm models: JPR, MLR, Back Propagation (BP) neural network, and Random Forest (RF). The JP–MLR model, which has an R2 value of 95.77%, has superior prediction performance when compared to the traditional regression-based JPR model and MLR model. It also performs better than the machine learning-based BP model and is identical to that of the RF model. This demonstrates that the JP–MLR model has satisfactory prediction performance and offers building operators an effective prediction tool. The proposed research method also provides also serves as a reference for electricity consumption analysis in other types of buildings. [ABSTRACT FROM AUTHOR]

Random forest (RF) is one of the most popular machine learning (ML) models used for both classification and regression problems. As an ensemble model, it demonstrates high predictive accuracy and low variance, while being easy to learn and optimize. In this study, we use RF for short-term load forecasting (STLF), focusing on data representation and training modes. We consider seven methods of defining input patterns and three training modes: local, global and extended global. We also investigate key RF hyperparameters to learn about their optimal settings. The experimental part of the work demonstrates on four STLF problems that our model, in its optimal variant, can outperform both statistical and ML models, providing the most accurate forecasts. [ABSTRACT FROM AUTHOR]

The effective pattern recognition of cable partial discharges (PDs) enables prompt corresponding measures to ensure cable safety. Traditional PD monitoring methods have limitations in online monitoring and accurate positioning, and the feature extraction of the monitored electrical signals requires significant prior knowledge. Therefore, this paper reports the performance of the distributed optical-fiber vibration-sensing monitoring of PDs on a cable with different insulation defects, and proposes a data-driven recognition approach based on the monitoring signals. The time series of the backscattered Rayleigh light intensity (BRLI) changes at the PD position were collected as the sample data. The coefficients of the time series' autoregressive moving average (ARMA) models were extracted as features. Next, a classification model trained by the random forest (RF) algorithm was established. After the model's validation with the experimental data and a comparative analysis with previously published methods, the PD recognition model was simply optimized based on the RF principle. The results showed that the proposed method achieved a high recognition accuracy, of about 98%, indicating that the data-driven approach—combining the ARMA model and the RF—is effective for cable-PD pattern recognition in distributed optical-fiber vibration-sensing systems. [ABSTRACT FROM AUTHOR]

Machine learning (ML) could be used to overcome one of the largest sources of uncertainty in wind resource assessment: to accurately predict the wind speed (WS) at the wind turbine hub height. Therefore, this research defined and evaluated the performance of seven ML supervised algorithms (regressions, decision tree, support vector machines, and an ensemble method) trained with meteorological mast data (temperature, humidity, wind direction, and wind speeds at 50 and 75 m), and mesoscale data below 80 m (from the New European Wind Atlas) to predict the WS at the height of 102 m. The results were compared with the conventional method used in wind energy assessments to vertically extrapolate the WS, the power law. It was proved that the ML models overcome the conventional method in terms of the prediction errors and the coefficient of determination. The main advantage of ML over the power-law was that ML performed the task using either only mesoscale data (described in scenario A), only data from the measurement mast (described in scenario B) or combining these two data sets (described in scenario C). The best ML models were the ensemble method in scenario A with an R 2 of 0.63, the linear regression in scenario B with an R 2 of 0.97, and the Ridge regressor in scenario C with an R 2 of 0.97. [ABSTRACT FROM AUTHOR]

Photovoltaic power generation has high variability and uncertainty because it is affected by uncertain factors such as weather conditions. Therefore, probabilistic forecasting is useful for optimal operation and risk hedging in power systems with large amounts of photovoltaic power generation. However, deterministic forecasting is the mainstay of photovoltaic generation forecasting; there are few studies on probabilistic forecasting and feature selection from weather or time-oriented features in such forecasting. In this study, prediction intervals were generated by the lower upper bound estimation (LUBE) using neural networks with two outputs to make probabilistic modeling for predictions. The objective was to improve prediction interval coverage probability (PICP), mean prediction interval width (MPIW), continuous ranked probability score (CRPS), and loss, which is the integration of PICP and MPIW, by removing unnecessary features through feature selection. When features with high gain were selected by random forest (RF), in the modeling of 14.7 kW PV systems, loss improved by 1.57 kW, CRPS by 0.03 kW, PICP by 0.057 kW, and MPIW by 0.12 kW on average over two weeks compared to the case where all features were used without feature selection. Therefore, the low gain features from RF act as noise and reduce the modeling accuracy. [ABSTRACT FROM AUTHOR]

In predicting energy consumption, classic econometric and statistical models are used to forecast energy consumption. These models may have limitations in an increasingly fast-changing energy market, which requires big data analysis of energy consumption patterns and relevant variables using complex mathematical tools. In current literature, there are minimal comparison studies reviewing machine learning algorithms to predict energy consumption in Korea. To bridge this gap, this paper compared three different machine learning algorithms, namely the Random Forest (RF) model, XGBoost (XGB) model, and Long Short-Term Memory (LSTM) model. These algorithms were applied in Period 1 (prior to the onset of the COVID-19 pandemic) and Period 2 (after the onset of the COVID-19 pandemic). Period 1 was characterized by an upward trend in energy consumption, while Period 2 showed a reduction in energy consumption. LSTM performed best in its prediction power specifically in Period 1, and RF outperformed the other models in Period 2. Findings, therefore, suggested the applicability of machine learning to forecast energy consumption and also demonstrated that traditional econometric approaches may outperform machine learning when there is less unknown irregularity in the time series, but machine learning can work better with unexpected irregular time series data. [ABSTRACT FROM AUTHOR]

Thermodynamic models are usually employed to predict formation condition of hydrates. However, these thermodynamic models usually require a large amount of calculations to approach phase equilibrium. Additionally, parameters included in the thermodynamic model need to be calibrated based on the experimental data, which leads to high uncertainties in the predicted results. With the rapid development of artificial intelligence (AI), machine learning as one of sub-discipline has been developed and been widely applied in various research area. In this work, machine learning was innovatively employed to predict the formation condition of natural gas hydrates to overcome the high computation cost and low accuracy. Three data-driven models, Random Forest (RF), Naive Bayes (NB), Support Vector Regression (SVR) were tentatively used to determine the formation condition of hydrate formed by pure and mixed gases. Experimental data reported in previous work were taken to train and test the machine learning models. As a representative thermodynamic model the Chen–Guo (C-G) model was used to analyze the computational efficiency and accuracy of machine learning models. The comparison of results predicted by C-G model and machine learning models with the experimental data indicated that the RF model performed better than the NB and SVR models on both computation speed and accuracy. According to the experimental data, the average AADP calculated by the C-G model is 7.62 times that calculated by the RF model. Meanwhile, the average time costed by the C-G model is 75.65 times that by the RF model. Compared with the other two machine learning models, the RF model is expected to be used in predicting the formation condition of natural gas hydrate under field conditions. [ABSTRACT FROM AUTHOR]

One of the most critical aspects of integrating renewable energy sources into the smart grid is photovoltaic (PV) power generation forecasting. This ensemble forecasting technique combines several forecasting models to increase the forecasting accuracy of the individual models. This study proposes a regression-based ensemble method for day-ahead PV power forecasting. The general framework consists of three steps: model training, creating the optimal set of weights, and testing the model. In step 1, a Random forest (RF) with different parameters is used for a single forecasting method. Five RF models (RF1, RF2, RF3, RF4, and RF5) and a support vector machine (SVM) for classification are established. The hyperparameters for the regression-based method involve learners (linear regression (LR) or support vector regression (SVR)), regularization (least absolute shrinkage and selection operator (LASSO) or Ridge), and a penalty coefficient for regularization (λ). Bayesian optimization is performed to find the optimal value of these three hyperparameters based on the minimum function. The optimal set of weights is obtained in step 2 and each set of weights contains five weight coefficients and a bias. In the final step, the weather forecasting data for the target day is used as input for the five RF models and the average daily weather forecasting data is also used as input for the SVM classification model. The SVM output selects the weather conditions, and the corresponding set of weight coefficients from step 2 is combined with the output from each RF model to obtain the final forecasting results. The stacking recurrent neural network (RNN) is used as a benchmark ensemble method for comparison. Historical PV power data for a PV site in Zhangbin Industrial Area, Taiwan, with a 2000 kWp capacity is used to test the methodology. The results for the single best RF model, the stacking RNN, and the proposed method are compared in terms of the mean relative error (MRE), the mean absolute error (MAE), and the coefficient of determination (R2) to verify the proposed method. The results for the MRE show that the proposed method outperforms the best RF method by 20% and the benchmark method by 2%. [ABSTRACT FROM AUTHOR]

Climate change, global warming, pollution, and energy crisis are the major growing concerns of this era, which have initiated the electrification of transport. The electrification of roadway transport has the potential to drastically reduce pollution and the growing demand for energy and to increase the load demand of the power grid, thereby giving a rise to technological and commercial challenges. Thus, charging load prediction is a crucial and demanding issue for maintaining the security and stability of power systems. During recent years, random forest has gained a lot of popularity as a powerful machine learning technique for classification as well as regression analysis. This work develops a random forest (RF)-based approach for predicting charging demand. The proposed method is validated for the prediction of public e-bus charging demand in the city of Helsinki, Finland. The simulation results demonstrate the effectiveness of our scheme. [ABSTRACT FROM AUTHOR]

The indicated mean effective pressure (IMEP) is a key parameter for measuring the power output of an internal combustion engine (ICE). This indicator can be used to locate the high efficiency regions of engines. Therefore, it makes sense to predict the IMEP based on the machine learning (ML) approaches. However, different ML models are applicable to different scenarios, so it is important to choose the right model for prediction. The objective of this paper was to compare three ML models' (ANN, SVR, RF) predictive performance in forecasting IMEP indicator with the input parameters spark timing (ST), speed and load. A validated one-dimensional (1D) computational fluid dynamics (CFD) model was employed to provide 756 sets of data for the training, validation, and testing of the model. The results indicated that the random forest (RF) model had the worst prediction performance, and support vector regression (SVR) had a slightly better prediction performance than the artificial neural network (ANN), at least for the investigations in this study. Overall, the ANN and SVR models showed good predictive performance for IMEP, as the coefficient of determination (R2) was close to unity, and the root mean squared error (RMSE) was close to zero. Whereas the overall prediction results of the RF model are acceptable, the RF model does not learn well for some internal engine laws. [ABSTRACT FROM AUTHOR]

Coal dust explosion constitutes a significant hazard in underground coal mines, coal power plants and other industries utilising coal as fuel. Knowledge of the explosion mechanism and the factors causing coal explosions is essential to investigate for the identification of the controlling factors for preventing coal dust explosions and improving safety conditions. However, the underlying mechanism involved in coal dust explosions is rarely studied under Artificial Intelligence (AI) based modelling. Coal from three different regions of Khyber Pakhtunkhwa, Pakistan, was tested for explosibility in 1.2 L Hartmann apparatus under various particle sizes and dust concentrations. First, a random forest algorithm was used to model the relationship between inputs (coal dust particle size, coal concentration and gross calorific value (GCV)), outputs (maximum pressure (Pmax) and the deflagration index (Kst)). The model reported an R2 value of 0.75 and 0.89 for Pmax and Kst. To further understand the impact of each feature causing explosibility, the random forest AI model was further analysed for sensitivity analysis by SHAP (Shapley Additive exPlanations). The study revealed that the most critical parameter affecting the explosibility of coal dust were particle size > GCV > concentration for Pmax and GCV > Particle size > Concentration for Kst. Mutual interaction SHAP plots of two variables at a time revealed that with <200 gm/L concentration, −73 µm size and a high GCV coal was the most explosive at a high concentration (>400 gm/L), explosibility is relatively lower irrespective of GCV and particle sizes. [ABSTRACT FROM AUTHOR]

Crop byproducts are alternatives to nonrenewable energy resources. Burning biomass results in lower emission of undesirable nitrogen and sulfur oxides and contributes no significant greenhouse effect. There is a diverse range of energy-useful biomass, including in terms of calorific value. This article presents a new method of discriminating biomass, and of determining its calorific value. The method involves extracting the selected texture features on the surface of a briquette from a microscopic image and then classifying them using supervised classification methods. The fractal dimension, local binary pattern (LBP), and Haralick features are computed and then classified by linear discrimination analysis (LDA). The discrimination results are compared with the results obtained by random forest (RF) and deep neural network (DNN) type classifiers. This approach is superior in terms of complexity and operating time to other methods such as, for instance, the calorimetric method or analysis of the chemical composition of elements in a sample. In the normal operation mode, our method identifies the calorific value in the time of about 100 s, i.e., 90 times faster than traditional combustion of material samples. In predicting from a single sample image, the overall average accuracy of 95% was achieved for all tested classifiers. The authors' idea to use ten input images of the same material and then majority voting after classification increases the discrimination system accuracy above 99%. [ABSTRACT FROM AUTHOR]

Commercial buildings are a significant consumer of energy worldwide. Logistics facilities, and specifically warehouses, are a common building type which remain under-researched in the demand-side energy forecasting literature. Warehouses have an idiosyncratic profile when compared to other commercial and industrial buildings with a significant reliance on a small number of energy systems. As such, warehouse owners and operators are increasingly entering energy performance contracts with energy service companies (ESCOs) to minimise environmental impact, reduce costs, and improve competitiveness. ESCOs and warehouse owners and operators require accurate forecasts of their energy consumption so that precautionary and mitigation measures can be taken. This paper explores the performance of three machine learning models (Support Vector Regression (SVR), Random Forest, and Extreme Gradient Boosting (XGBoost)), three deep learning models (Recurrent Neural Networks (RNNs), Long Short-Term Memory (LSTM), and Gated Recurrent Unit (GRU)), and a classical time series model, Autoregressive Integrated Moving Average (ARIMA) for predicting daily energy consumption. The dataset comprises 8040 records generated over an 11-month period from January to November 2020 from a non-refrigerated logistics facility located in Ireland. The grid search method was used to identify the best configurations for each model. The proposed XGBoost models outperformed other models for both very short-term load forecasting (VSTLF) and short-term load forecasting (STLF); the ARIMA model performed the worst. [ABSTRACT FROM AUTHOR]

Natural gas consumption depends on many factors. Some of them, such as weather conditions or historical demand, can be accurately measured. The authors, based on the collected data, performed the modeling of temporary and future natural gas consumption by municipal consumers in one of the medium-sized cities in Poland. For this purpose, the machine learning algorithms, neural networks and two regression algorithms, MLR and Random Forest were used. Several variants of forecasting the demand for natural gas, with different lengths of the forecast horizon are presented and compared in this research. The results obtained using the MLR, Random Forest, and DNN algorithms show that for the tested input data, the best algorithm for predicting the demand for natural gas is RF. The differences in accuracy of prediction between algorithms were not significant. The research shows the differences in the impact of factors that create the demand for natural gas, as well as the accuracy of the prediction for each algorithm used, for each time horizon. [ABSTRACT FROM AUTHOR]

Heavy-duty trucks contribute approximately 20% of fuel consumption in the United States of America (USA). The fuel economy of heavy-duty vehicles (HDV) is affected by several real-world parameters like road parameters, driver behavior, weather conditions, and vehicle parameters, etc. Although modern vehicles comply with emissions regulations, potential malfunction of the engine, regular wear and tear, or other factors could affect vehicle performance. Predicting fuel consumption per trip based on dynamic on-road data can help the automotive industry to reduce the cost and time for on-road testing. Data modeling can easily help to diagnose the reason behind fuel consumption with a knowledge of input parameters. In this paper, an artificial neural network (ANN) was implemented to model fuel consumption in modern heavy-duty trucks for predicting the total and instantaneous fuel consumption of a trip based on very few key parameters, such as engine load (%), engine speed (rpm), and vehicle speed (km/h). Instantaneous fuel consumption data can help to predict patterns in fuel consumption for optimized fleet operations. In this work, the data used for modeling was collected at a frequency of 1Hz during on-road testing of modern heavy-duty vehicles (HDV) at the West Virginia University Center for Alternative Fuels Engines and Emissions (WVU CAFEE) using the portable emissions monitoring system (PEMS). The performance of the artificial neural network was evaluated using mean absolute error (MAE) and root mean square error (RMSE). The model was further evaluated with data collected from a vehicle on-road trip. The study shows that artificial neural networks performed slightly better than other machine learning techniques such as linear regression (LR), and random forest (RF), with high R-squared (R2) and lower root mean square error. [ABSTRACT FROM AUTHOR]

To effectively analyze building energy, it is important to utilize the environmental data that influence building energy consumption. This study analyzed outdoor and indoor data collected from buildings to find out the conditions of rooms that had a significant effect on heating and cooling energy consumption. To examine the conditions of the rooms in each building, the energy consumption importance priority was derived using the Gini importance of the random forest algorithm on external and internal environmental data. The conditions that had a significant effect on energy consumption were analyzed to be: (i) conditions related to the building design—wall, floor, and window area ratio, the window-to-wall ratio (WWR), the window-to-floor area ratio (WFR), and the azimuth, and (ii) the internal conditions of the building—the illuminance, occupancy density, plug load, and frequency of room utilization. The room conditions derived through analysis were considered in each sample, and the final influential building energy consumption factors were derived by using them in a decision tree as being the WFR, window area ratio, floor area ratio, wall area ratio, and frequency of use. Furthermore, four room types were classified by combining the room conditions obtained from the key factor classifications derived in this study. [ABSTRACT FROM AUTHOR]

Ethanol is one of the most used fuels in Brazil, which is the second-largest producer of this biofuel in the world. The uncertainty of price direction in the future increases the risk for agents operating in this market and can affect a dependent price chain, such as food and gasoline. This paper uses the architecture of recurrent neural networks—Long short-term memory (LSTM)—to predict Brazilian ethanol spot prices for three horizon-times (12, 6 and 3 months ahead). The proposed model is compared to three benchmark algorithms: Random Forest, SVM Linear and RBF. We evaluate statistical measures such as MSE (Mean Squared Error), MAPE (Mean Absolute Percentage Error), and accuracy to assess the algorithm robustness. Our findings suggest LSTM outperforms the other techniques in regression, considering both MSE and MAPE but SVM Linear is better to identify price trends. Concerning predictions per se, all errors increase during the pandemic period, reinforcing the challenge to identify patterns in crisis scenarios. [ABSTRACT FROM AUTHOR]

The quality of feedstock used in base oil processing depends on the source of the crude oil. Moreover, the refinery is fed with various blends of crude oil to meet the demand of the refining products. These circumstances have caused changes of quality of the feedstock for the base oil production. Often the feedstock properties deviate from the original properties measured during the process design phase. To recalculate and remodel using first principal approaches requires significant costs due to the detailed material characterizations and several pilot-plant runs requirements. To perform all material characterization and pilot plant runs every time the refinery receives a different blend of crude oil will simply multiply the costs. Due to economic reasons, only selected lab characterizations are performed, and the base oil processing plant is operated reactively based on the feedback of the lab analysis of the base oil product. However, this reactive method leads to loss in production for several hours because of the residence time as well as time required to perform the lab analysis. Hence in this paper, an alternative method is studied to minimize the production loss by reacting proactively utilizing machine learning algorithms. Support Vector Regression (SVR), Decision Tree Regression (DTR), Random Forest Regression (RFR) and Extreme Gradient Boosting (XGBoost) models are developed and studied using historical data of the plant to predict the base oil product kinematic viscosity and viscosity index based on the feedstock qualities and the process operating conditions. The XGBoost model shows the most optimal and consistent performance during validation and a 6.5 months plant testing period. Subsequent deployment at our plant facility and product recovery analysis have shown that the prediction model has facilitated in reducing the production recovery period during product transition by 40%. [ABSTRACT FROM AUTHOR]

A fault diagnosis method for wind turbine gearboxes based on undersampling, XGBoost feature selection, and improved whale optimization-random forest (IWOA-RF) was proposed for the problem of high false negative and false positive rates in wind turbine gearboxes. Normal samples of raw data were subjected to undersampling first, and various features and data labels in the raw data were provided with importance analysis by XGBoost feature selection to select features with higher label correlation. Two parameters of random forest algorithm were optimized via the whale optimization algorithm to create a fitness function with the false negative rate (FNR) and false positive rate (FPR) as evaluation indexes. Then, the minimum fitness function value within the given scope of parameters was found. The WOA was controlled by the hyper-parameter α to optimize the step size. This article uses the variant form of the sigmoid function to alter the change trend of the WOA hyper-parameter α from a linear decline to a rapid decline first and then a slow decline to allow the WOA to be optimized. In the initial stage, a larger step size and step size change rate can make the model progress to the optimization target faster, while in the later stage of optimization, a smaller step size and step size change rate allows the model to more accurately find the minimum value of the fitness function. Finally, two hyper-parameters, corresponding to the minimum fitness function value, were substituted into a random forest algorithm for model training. The results showed that the method proposed in this paper can significantly reduce the false negative and false positive rates compared with other optimization classification methods. [ABSTRACT FROM AUTHOR]