Evaluating machine learning algorithms for energy consumption prediction in electric vehicles: A comparative study

Travel planning is a process that allows users to obtain maximum benefit from their time, cost and energy. When planning a route from one place to another, it is an important option to present alternative travel areas on the route. This study proposes a travel route planning (TRP) architecture using a Long Short-Term Memory (LSTM) and Extreme Gradient Boosting (XGBoost) model to improve both travel efficiency and environmental sustainability in route selection. This model incorporates carbon emissions directly into the route planning process by unifying user preferences, location recommendations, route optimization, and multimodal vehicle selection within a comprehensive framework. By merging environmental sustainability with user-focused travel planning, it generates personalized, practical, and low-carbon travel routes. The carbon emissions observed with TRP’s artificial intelligence (AI) recommendation route are presented comparatively with those of the user-determined route. XGBoost, Random Forest (RF), Categorical Boosting (CatBoost), Light Gradient Boosting Machine (LightGBM), (Extra Trees Regressor) ETR, and Multi-Layer Perception (MLP) models are applied to the TRP model. LSTM is compared with Recurrent Neural Networks (RNNs) and Gated Recurrent Unit (GRU) models. Root Mean Square Error (RMSE), Mean Absolute Error (MAE), Mean Squared Error (MSE), and Normalized Root Mean Square Error (NRMSE) error measurements of these models are carried out, and the best result is obtained using XGBoost and LSTM. TRP enhances environmental responsibility awareness within travel planning by integrating sustainability-oriented parameters into the decision-making process. Unlike conventional reservation systems, this model encourages individuals and organizations to prioritize eco-friendly options by considering not only financial factors but also environmental and socio-cultural impacts. By promoting responsible travel behaviors and supporting the adoption of sustainable tourism practices, the proposed approach contributes significantly to the broader dissemination of environmentally conscious travel choices.

The intricacies and instability of introducing cryogenic propellants into the combustion system have piqued the curiosity of scientists studying the procedure. The latest innovation is utilizing data-driven machine learning and deep learning approaches to gain deeper insights into the related difficulties. However, the current work serves as a baseline for future research because relatively few studies have used data-driven methodologies to assess the temperature of liquid fuel injections in combustion systems. The performance of Linear Regression (LR), Random Forest (RF), Extra Trees Regressor (ETR), Polynomial Regression (PR), Support Vector Regressor (SVR), Decision Tree Regressor (DTR), Gradient Boost Regressor (GBR), XGB Regressor (XGBoost), AdaBoost Regressor (ABR), K-Neighbors Regressor (KNR), Long-Short Term Memory (LSTM), Bi-LSTM (Bi-directional Long-Short Term Memory) has all been investigated in this study. The study also suggested a Fully Connected Neural Network (FCNN) to examine its performance and paired it with an Extra Tree Regressor (ETR). The coupled FCNN and Extra Tree Regressor outperform the other algorithms with a Mean Square Error (MSE) of 0.0000005062, Root Mean Square Error (RMSE) of 0.00071148, Mean Absolute Error (MAE) of 0.00020672, and R-squared (R2) value of 0.99998689. Linear Regression, Polynomial Regression, and Support Vector Regressor are found to be the least-performing algorithms. The current work uses machine learning and deep learning methods to make data-driven decisions for liquid fuel injection in the combustion system.

Accurate energy consumption prediction is a critical resource allocation, reducing operational costs, and supporting sustainable energy management. This study presents a machine learning-based energy consumption prediction system, comparing the performance of Linear Regression, Ridge Regression, Lasso Regression, and Decision tree models. Using a historical dataset of consumer energy usage, key features were extracted and preprocessed to enhance predictive accuracy. Model evaluation employed Mean Squared Error (MSE), Root Mean Squared Error (RMSE), and Coefficient of Determination metrics. Results indicate that while Linear Regression offered competitive baseline performance, Ridge and Lasso Regressions achieved improved stability against multicollinearity, and the Decision Tree Model delivered the highest predictive accuracy, albeit with potential overfitting risks. The findings contribute to developing scalable, data-driven forecasting frameworks applicable to smart grids, policy planning, and demand-side management.

The smart buildings’ load forecasting is necessary for efficient energy management, and it is easily possible because of the data availability based on widespread use of Internet of Things (IoT) devices and automation systems. The information of buildings’ occupancy is directly associated with energy consumption. Therefore, we present a hybrid model consisting of a Long Short-Term Memory (LSTM) network, Extreme Gradient Boosting (XgBoost), Random Forest (RF) and Linear Regression (LR) for commercial and academic buildings’ load forecasting. The correlation between occupants’ count and total load of the building is calculated using Pearson Correlation Coefficient (PCC). The comparative analysis of the proposed approach with LSTM, XgBoost, RF and Gated Recurrent Unit (GRU) is also performed. Root Mean Square Error (RMSE), Mean Absolute Error (MAE), Mean Square Error (MSE) and Normalized Root Mean Square Error (NRMSE) are used as performance indicators for evaluating performance. Findings indicate that the proposed hybrid approach outperforms other models. The RMSE and MAE of 2.99 and 2.18, respectively, are recorded by the proposed model for commercial building dataset while for academic building the RMSE and MAE are 4.48 and 2.85, respectively. Occupancy and load consumption have a positive correlation as evident from PCC analysis. Therefore, we have scheduled the forecasted load based on occupancy patterns for two different cases. Cost is reduced by 17.42% and 33.40% in case 1 and case 2, respectively. Moreover, the performance of the proposed hybrid approach is compared with different techniques presented in literature for buildings load forecasting.

Accurate forecasting of economic indicators is essential for informed policy-making and strategic financial planning. This study presents a comprehensive comparative statistical analysis of multiple machine learning regression models—including linear regression, ridge regression, lasso regression, support vector regression, and random forest regression—for predicting key economic indicators such as GDP growth, unemployment rate, and inflation. Using a simulated dataset of 1000 samples with multiple economic features, each model’s performance was evaluated using mean squared error (MSE), mean absolute error (MAE), root mean squared error (RMSE), and coefficient of determination ($R^2$). Results indicate that ensemble methods such as random forest provide superior predictive accuracy, while regularized models outperform ordinary linear regression. Visualizations of predicted versus actual values, residual analyses, and statistical comparisons are provided to support the findings. Implications for model selection and economic forecasting are discussed.

The prediction of wind energy generation is important to enhance the performance and dependability of renewable energy systems due to the rising demand for wind-generated electricity and advancements in wind energy technology competitiveness. This study leverages advanced machine learning (ML) and some other statistical and deep learning based time series forecasting models to enhance the accuracy of wind energy predictions. This comprehensive analysis includes nine ML models—Linear Regression, Random Forests (RF), Gradient Boosting Machines (GBM), Support Vector Machines (SVM), K-Nearest Neighbors (KNN), AdaBoost, XGBoost, Support Vector Regression (SVR), and Neural Networks—as well as Four time-series forecasting models—ARIMA, Temporal Convolutional Networks (TCNs), Long Short-Term Memory (LSTM) networks and GRU. Each ML model underwent rigorous cross-validation to ensure optimal performance. The assessment criteria utilized here comprised the Mean Squared Error (MSE), Root Mean Squared Error (RMSE), Mean Absolute Error (MAE), and the R² Score. It was found that among the nine ML models, Random Forests, GBM and KNN consistently provided superior accuracy and robustness, making them the top choices for wind energy prediction whereas the performance of linear regression, SVM and SVR were very poor for the considered dataset. From the experiment, Random Forest, GBM, and KNN showed the best performance with low MSE values of 0.77, 1.95, and 1.51 respectively, while other models had MSEs above 7.5, with AdaBoost reaching 30. Their RMSEs (0.88, 1.40, 1.23) and MAEs (0.093, 0.73, 0.10) also indicate strong predictive accuracy compared to the rest.In this paper, time series forecasting, TCNs, LSTM and GRU networks showed strong capabilities in capturing temporal dependencies and trends within the wind energy data. Visualization techniques were employed to compare model performances comprehensively, providing clear insights into their predictive power. Therefore, this present study offers a robust framework for researchers and practitioners aiming to leverage machine learning and time series forecasting in the realm of renewable energy prediction.

Accurate forecast of carbon dioxide (CO2) emissions plays a significant role in China's carbon peaking and carbon neutrality policies. A novel two-stage forecast procedure based on support vector regression (SVR), random forest (RF), ridge regression (Ridge), and artificial neural network (ANN) is proposed and evaluated by comparing it with the single-stage forecast procedure. Nine independent variables’ data (study period: 1985–2020) are used to forecast the CO2 emissions in China. Our results reveal that, when the time gap, h increases from 1 to 8, the average root mean squared error (RMSE) and mean absolute error (MAE) of SVR–SVR, SVR–RF, SVR–Ridge, and SVR–ANN are almost uniformly lower than errors arising from their single-stage version, respectively. Among these two-stage models, SVR–ANN exhibits the lowest forecast errors, whereas SVR–RF admits the highest. The mean percentage decrease in forecast errors of SVR–SVR vs. SVR, SVR–RF vs. RF, SVR–Ridge vs. Ridge, and SVR–ANN vs. ANN are 36.06, 5.98, 43.05, and 14.81 for RMSE, and 36.06, 6.91, 43.27, and 15.35 for MAE. Our two-stage procedure is also suitable to forecast other variables, such as fossil fuel and renewable energy consumption.

Time-Scale Modification (TSM) is a well-researched field and allows for time-domain manipulation of a signal without modifying the pitch or timbre. Many TSM methods have been presented, however quantitative results on the quality of these methods are rare, with most methods reporting informal listening tests. This is likely due to the timecommitment and cost of subjective testing. Additionally, an objective measure of quality has not yet been developed that is suitable for timescaled signals. This dissertation describes the design of e ective objective measures of quality for TSM. TSM methods are, generally, single channel algorithms that give poor results when applied to multi-channel signals, as the phase relationship between channels must be maintained. This dissertation proposes a method and additional variant for maintaining the phase relationship between channels and retaining the presence in the centre of the stereo signal. The method involves pre- and post-processing the signal, with the variant processing each frame for real-time suitability. Sum and di erence transformations of the stereo signal are used for TSM and result in a large improvement in stereo phase coherence, consequently maintaining the stereo field. The proposed method produces a highquality stereo output and greatly improves quality over the independent channel processing method. It also allows for simple implementation around all existing TSM frameworks. A modification to the Epoch-Synchronous Overlap-Add (ESOLA) TSM algorithm is proposed in this dissertation. The proposed method, Fuzzy Epoch-Synchronous Overlap-Add, improves on the previous ESOLA method through cross-correlation of time-smeared epochs before overlap-adding. This reduces distortion and artefacts while the speaker's fundamental frequency is stable, as well as reducing artefacts during pitch modulation. The proposed method is tested against well-known TSM algorithms. It is preferred over ESOLA and gives similar performance to other TSM algorithms for voice signals. It is also shown that this algorithm can work effectively with solo instrument signals containing strong fundamental frequencies. No effective objective measure of quality for TSM exists. This dissertation details the creation, subjective evaluation and analysis of a dataset, for use in the development of an objective measure of quality for TSM. Comprising two parts, the training subset contains 88 source files processed using six TSM methods at 10 time-scales, while the testing subset contains 20 source files processed using three additional methods at four time-scales. The source material contains speech, solo harmonic and percussive instruments, sound effects and a range of music genres. 42,529 ratings were collected from 633 sessions using laboratory and remote collection methods. Analysis of results shows no correlation between age and quality of rating; equivalence between expert and non-expert listeners; negligible di erences between participants with and without hearing issues; and negligible di erences between testing modalities. Comparison of published objective measures and subjective scores shows the objective measures to be poor indicators of subjective quality. Initial results for a retrained objective measure of quality are presented with results approaching average loss and correlation values of subjective sessions. An objective measure of quality for time-scaled audio is proposed that makes use of the previously developed dataset and improves on reported results. The measure uses hand-crafted features and a fully connected network to predict subjective mean opinion scores. Basic and Advanced Perceptual Evaluation of Audio Quality features are used in addition to nine features specific to TSM artefacts. Six methods of alignment are explored, with interpolation of the reference magnitude spectrum to the length of the test magnitude spectrum giving the best performance. The proposed measure achieves an average Root Mean Squared Error (RMSE) of 0.490 and a mean Pearson Correlation Coe cient (PCC) of 0.864, equivalent to 97th and 82nd percentiles of subjective sessions respectively. The proposed measure is used to evaluate TSM algorithms, finding that Elastique gives the highest objective quality for solo instrument and voice signals, while the Identity Phase-Locking Phase Vocoder gives the highest objective quality for music signals and the best overall quality. Two single-ended objective quality measures for time-scaled audio are also proposed. These measure do not require a reference signal, nor alignment. Data driven features are created by either a convolutional neural network (CNN) or a bidirectional gated recurrent unit (BGRU) network, and are fed to a fully-connected network to predict subjective mean opinion scores. The proposed CNN and BGRU measures achieve an average RMSE of 0.608 and 0.576, and a mean PCC of 0.771 and 0.794, respectively. The proposed measures are used to evaluate TSM algorithms, and comparisons are provided for 16 TSM implementations. A literature review is included with required background knowledge. It includes the fundamentals of sound perception, sound capture, digital signal processing, time-scale modification methods used within research, and subjective and objective measures of quality. Full implementation of all proposed methods and measures can be found at github.com/zygurt/TSM, while the labelled dataset is available at http://ieee-dataport.org/1987.

In this study, the artificial neural networks (ANNs) and regression models were used to downscale the simulated outputs of the general circulation models (GCMs). The simulated precipitation for 25.18 º N to 34.51 º N and 45 º E to 60 º E, geopotential height at 850 mb and zonal wind at 200 mb for 12.56° N to 43.25° N and 19.68° E to 61.87° E data sets as the predictors were extracted from ECHAM5 GCM for the period 1960-2005. The observed monthly precipitation data of Abadan, Abadeh, Ahwaz, Bandar Abbas, Bushehr, Shiraz and Fasa stations as the predictand were extracted for the period 1960-2005. The principal components (PCs) of the simulated data sets were extracted and then six PCs were considered as the input file of the ANN and multiple regression models. Also the combinations of the simulated data sets were used as the input file of these models. The periods 1960-2000 and 2001-2005 were considered as the train and test data in the ANN, respectively. The Pearson correlation coefficient and normalized root mean square error results indicated that ANN predicts precipitation more accurate than multiple regression. For the monthly time scale, the geopotential height is the best predictor and for the seasonal time scale (winter) the simulated precipitation is the best predictor in ANN based standardized precipitation principal components.

Models of river flow time series are essential in efficient management of a river basin. It helps policy makers in developing efficient water utilization strategies to maximize the utility of scarce water resource. Time series analysis has been used extensively for modeling river flow data. The use of machine learning techniques such as support-vector regression and neural network models is gaining increasing popularity. In this paper we compare the performance of these techniques by applying it to a long-term time-series data of the inflows into the Krishnaraja Sagar reservoir (KRS) from three tributaries of the river Cauvery. In this study flow data over a period of 30 years from three different observation points established in upper Cauvery river sub-basin is analyzed to estimate their contribution to KRS. Specifically, ANN model uses a multi-layer feed forward network trained with a back-propagation algorithm and support vector regression with epsilon intensive-loss function is used. Auto-regressive moving average models are also applied to the same data. The performance of different techniques is compared using performance metrics such as root mean squared error (RMSE), correlation, normalized root mean squared error (NRMSE) and Nash-Sutcliffe Efficiency (NSE).

The purpose of this study is to evaluate the effectiveness of various machine learning algorithms in predicting the ultimate tensile strength (UTS) of friction stir welded joints. This prediction is crucial for assessing weld quality and integrity. Several investigations are carried out in linear and non-linear regression models, including Poisson Regressor, Gradient Boosting Regressor, Bayesian Ridge, k-Nearest Neighbours, Lasso, Random Forest, Elastic-Net, and Support Vector Regression, using datasets of welding parameters. The models were evaluated for their prediction accuracy and reliability using mean absolute error (MAE), mean square error (MSE) and R2 score metrics. The findings revealed significant variations in model performance. Non-linear models, especially Decision Trees and k-Nearest Neighbours, have exhibited the highest efficiency. The Decision Tree model achieved a remarkable R2 score of 0.97. Meanwhile, the k-Nearest Neighbours model noted the lowest MSE value of 408.62, whereas the MAE value was 14.75. In comparison, the Support Vector Regression model lagged significantly, registering the best MSE and MAE of 14824.74 and 107.73, along the side of a negative R2 score of −0.22, indicating deficient performance. This study highlights the potential of machine learning to forecast the quality of the weld joints. It proposes that the utilisation of these models can significantly enhance production processes and optimise material performance in industrial applications. The study demonstrates the integration of machine learning into manufacturing workflows to enable predictive maintenance and enhance quality control.

River stream estimation is a subject matter that needs constant research and development since it is all-important in the management of water resources, meeting the water demand, irrigation and agricultural activities, and providing distant signal in unwanted situations such as floods. Unfortunately, a universal technique has been impossible to talk about yet although many techniques have been used for estimation and modeling. This has made it necessary to develop different techniques and / or to make comparisons between techniques and to determine the most accurate method for the parameters used. In this context, in this study, the distribution graphs of the flow data fourteen stations located in the Euphrates-Tigris Basin for the years 1981-2010 were created and their conformity to the normal distribution was investigated Evaluation has been made with Adaptive Neural Fuzzy Logic System (ANFIS), Support Vector Regression (SVR) techniques and the newly introduced Gaussian Process Regression (GPR), Extreme Learning Machine (ELM) and Emotional Neural Networks (ENN) artificial intelligence techniques. Root Mean Square Error (RMSE), Mean Square Error (MSE), Mean Absolute Error (MAE) and correlation coefficient (r), which are frequently used, were used to determine the model performance. In addition, it is aimed to find out which technique gives better results with rank analysis. Although all models work well, the sequence with regards to the comparison outcomes of the techniques obtained from rank analysis was observed to be ELM, GPR, ENN, SVR, ANFIS respectively. In order to better interpret the results, Taylor diagrams and time series graphics of the model outputs were created.

Accurate forecasting of photovoltaic (PV) generated electricity is essential for efficiently managing and integrating Renewable Energy (RE) into electricity distribution systems. This research investigation optimizes Feature Selection (FS) and prediction results for PV energy prediction by applying Bayesian Density Estimation (BDE) with Elastic Net (ELNET) regression analysis. This phenomenon and unacceptable outcomes are prevalent when applying conventional regression algorithms on datasets with significant results and addressing predictor multicollinearity. Improved FS and multicollinearity control has been rendered feasible by ELNET, which integrates the best features of Ridge and Lasso regression. ELNET eliminates these challenges through the implementation of L1 and L2 penalties. Non-parametric prediction Bayesian Density Estimation (BDE) is comprehensive data regarding residual distributions and predictor impacts. By incorporating ELNET’s regularisation and FS abilities with BDE’s statistical prediction and adaptability, the recommended ELNET-BDE is proposed to attain more accurate and reliable predictions. This technique has been used to assess massive data sets developing from Visakhapatnam, India, incorporating historical PV energy generation combined with definite Meteorological Factors (MF). Considering comprehensive data preliminary processing, FS, and validation, ELNET-BDE outperforms existing methods. Research investigations demonstrate that the ELNET-BDE model attains significantly lower Mean Absolute Error (MAE) and Root Mean Square Error (RMSE) than contesting Machine Learning (ML) algorithms like Artificial Neural Network (ANN), Support Vector Machine (SVM), Random Forest (RF), and Gradient Boosting Machines (GBM). Compared with distinct FS techniques, RMSE can be minimized by up to 15% and MAE by up to 20%. The findings specify a substantial improvement in accuracy in prediction, emphasizing how the model can be used for improving solar power grid integration and energy for improved RE management.

The wind power industry has experienced a significant increase and popularity in recent times, and the latest statistics indicate that this sector is still thriving. However, one of the essential steps in developing wind energy projects is finding suitable sites for wind farms, which involves understanding the nature of wind speed, wind direction, terrain, and environmental impacts. To predict the wind energy production over the expected lifespan of a wind farm, vertical wind speed extrapolation to the hub height of the wind turbine is necessary. Therefore, this study presents a comprehensive evaluation of seven statistical approaches for vertical wind speed extrapolation, including Generalized Linear Models (GLM), Linear Regression (LR), Support Vector Machines (SVM), Generalized Additive Models (GAM), Gaussian Process Regression (GPR), Regression Tree (RT), and Ensemble Regression (ER). The accuracy of these methods is assessed using performance metrics such as Mean Squared Error (MSE), Root Mean Squared Error (RMSE), Normalized RMSE (NRMSE), Normalized MSE (NMSE), Mean Bias Error (MBE), Mean Absolute Error (MAE), Mean Percentage Error (MPE), Mean Absolute Percentage Error (MAPE), Symmetric Mean Absolute Percentage Error (SMAPE), and R-squared (R2). The study concludes that, on average, GLM performs the best out of all seven statistical methods.

Since multi-omics data (high-dimensional data, including many variables, like genomics, transcriptomics and proteomics) are high-dimensional (have many variables), heterogeneous (are data of different types), and interdependent (variables can affect each other), there is a significant challenge in integrating various data types in a synergistic manner (without, it may not be feasible). This paper is an attempt to tackle the problem of predicting drug sensitivity by offering a hybrid machine learning (ML, algorithms that learn patterns by analyzing data) and deep learning (DL, a type of ML based on neural network architectures to learn patterns of data) model of not only predicting drug sensitivity, but also using Genomics of Drug Sensitivity in Cancer (GDSC) data. The method involves applying sophisticated preprocessing, strict feature selection techniques (Random Forest, tree-based algorithm; LASSO regression, feature selection that chooses features by pruning irrelevant ones; Mutual Information which is a dependency measure between variables) as well as a hybrid model design favoring classical ML models (Random Forest, SVM--Support Vector Machine) over the deep learning style (LSTM-Long short-term memory networks, a sequence-based version of recurrent neural network). The metrics of the evaluation give a very high R 2 (coefficient of determination, a measure of model fit) of 0.991, and the evaluations are very small MAE ( Mean absolute error: square root of average squared error), RMSE (Root mean squared error: square root of average squared error), and MSE ( Mean squared error: square root of average squared error), resulting in approximately 12 percent higher accuracy than conventional approximations: Ridge Regression, XGBoost (Extreme gradient boosting), and Random Forest alone. This also shows the strength (capacity to work with diverse circumstances), generalizability (capacity to be used in new information), and enhanced predictive capabilities of the hybrid method of modeling nonlinear associations in multi-omics information, thus enhancing precision medicine (health care customized to patients in terms of genetic, environmental, and lifestyle determinants) uses.