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With climate change driving the global push toward sustainable energy, the reliability of power systems increasingly depends on accurate forecasting methods. This study examined the role of machine learning (ML) in forecasting solar PV power output (SPVPO) and wind turbine power output (WTPO) and identified the challenges posed by the intermittent nature of these renewable energy sources. This study examined the current techniques, challenges, and future directions in ML-based forecasting of SPVPO and WTPO and proposed a standardized framework. Using the Mann–Whitney and Kruskal–Wallis tests, the results highlight the significant impact of key meteorological and operational variables on enhancing forecasting accuracy, as measured by MAPE and R-squared. Key features for SPVPO forecasting include solar irradiance, ambient temperature, and prior SPVPO, while wind speed, turbine speed, and prior wind power output are crucial for WTPO forecasting. Moreover, ensemble models, support vector machines, Gaussian processes, hybrid artificial neural networks, and decomposition-based hybrid models exhibit promising forecasting accuracy and reliability. Challenges such as data availability, complexity-interpretability trade-offs, and integration difficulties with energy management systems present opportunities for innovative solutions. These include exploring advanced data processing and calibration techniques, leveraging Big Data and IoT advancements, formulating advanced machine learning (ML) techniques, and employing probabilistic approaches with desirable accuracy and robustness in forecasting solar photovoltaic power output (SPVPO) and wind turbine power output (WTPO). Additionally, expanding research to ensure model generalizability across diverse climate conditions and forecasting horizons is crucial for enhancing the reliability and efficiency of renewable energy forecasting using machine learning techniques.

In the digital currency market, including Bitcoin, price prediction using artificial intelligence (AI) and machine learning (ML) is critical but challenging. Conventional methods such as technical analysis (based on historical market data) and fundamental analysis (based on economic variables) suffer from data noise, processing delays, and insufficient data. To make predictions more accurate, faster, and able to handle more data, the suggested method combines several steps: extracting important information, labeling it, choosing the best features, merging different models, and fine‐tuning the model settings. Based on the price data, this approach initially generates 5 labels with a new labeling method based on the percentage of average price changes in several days and generates signals (hold, buy, sell, strong sell, and strong buy). Thereafter, it extracts 768 features from technical studies using the TA‐Lib library and from an authoritative site. The TLBOA algorithm, which does not get stuck in the local optimum with two updates, was used to select and reduce features to 15 to avoid overfitting. A variety of ML models, including support vector machine and Naive Bayes, use these selected features for training. By using the evolutionary DE algorithm to optimize the XGBoost meta‐parameters, we increased the accuracy by 1%–4%. The proposed strategy has performed better than other models, such as XGBoost with 85.66% and gradient boosting with 84.15%, and has achieved an accuracy of 91%–92%.

The increasing adoption of solar power as a renewable and eco-friendly energy source necessitates precise forecasting of solar power generation. Accurate predictions are crucial for effective grid management and the seamless integration of renewable energy into the power grid. This study proposes a novel hybrid meta-heuristic optimization framework, empowered by an ensemble deep learning model, to enhance the accuracy of solar power generation forecasting. The proposed methodology comprises several methodical phases: data pre-processing, feature extraction, feature selection, and deep learning-based forecasting. Initially, the collected raw data undergo a pre-processing stage involving data cleaning and standardization via the z-score method. Subsequent feature extraction transforms the pre-processed data into a reduced set of representative features, leveraging Linear Discriminant Analysis (LDA), measures of central tendency (Weighted arithmetic mean, Winsorized mean, standard deviation), statistical dispersion (Interquartile range (IQR), Median absolute deviation (MAD)), and Information Theoretic measures (Mutual Information and Information Gain). The optimal features are then selected through a newly proposed hybrid optimization approach, the Gorilla Customized Teaching Learning-Based Optimization (GC-TLBO) Algorithm, an innovative combination of the Artificial Gorilla Troops Optimizer (GTO) and the Teaching-Learning-Based Optimization (TLBO). Solar power forecasting is accomplished using a novel ensembled deep learning model, which integrates optimized Recurrent Neural Network (O-RNN) with a Deep Belief Network (DBN) and a Deep Convolutional Neural Network (DCNN). The final outcome is derived from the O-RNN, which inputs the results from the DBN and DCNN, respectively. The DBN and DCNN are trained using the optimal features derived from the GC-TLBO, while the weights of the RNN are fine-tuned using the same algorithm. The proposed model was implemented in Python (Google Colab), and its performance was evaluated using several metrics: Normalized Mean Square Error (NMSE), Mean Squared Relative Error (MSRE), Mean Squared Error (MSE), Mean Absolute Percentage Error (MAPE), and Root Mean Squared Error (RMSE). The results demonstrate that the proposed model outperforms existing models, offering superior forecasting performance.

This paper aims to propose a new model for time series forecasting that combines forecasting with clustering algorithm. It introduces a new scheme to improve the forecasting results by grouping the time series data using k-means clustering algorithm. It utilizes the clustering result to get the forecasting data. There are usually some user-defined parameters affecting the forecasting results, therefore, a learning-based procedure is proposed to estimate the parameters that will be used for forecasting. This parameter value is computed in the algorithm simultaneously. The result of the experiment compared to other forecasting algorithms demonstrates good results for the proposed model. It has the smallest mean squared error of 13,007.91 and the average improvement rate of 19.83%.

In this paper, we present a highly accurate forecasting method that supports improved investment decisions. The proposed method extends the novel hybrid SVM-TLBO model consisting of a support vector machine (SVM) and a teaching-learning-based optimization (TLBO) method that determines the optimal SVM parameters, by combining it with dimensional reduction techniques (DR-SVM-TLBO). The dimension reduction techniques (feature extraction approach) extract critical, non-collinear, relevant, and de-noised information from the input variables (features), and reduce the time complexity. We investigated three different feature extraction techniques: principal component analysis, kernel principal component analysis, and independent component analysis. The feasibility and effectiveness of this proposed ensemble model were examined using a case study, predicting the daily closing prices of the COMDEX commodity futures index traded in the Multi Commodity Exchange of India Limited. In this study, we assessed the performance of the new ensemble model with the three feature extraction techniques, using different performance metrics and statistical measures. We compared our results with results from a standard SVM model and an SVM-TLBO hybrid model. Our experimental results show that the new ensemble model is viable and effective, and provides better predictions. This proposed model can provide technical support for better financial investment decisions and can be used as an alternative model for forecasting tasks that require more accurate predictions.

Predictive analysis in business process monitoring aims at forecasting the future information of a running business process. The prediction is typically made based on the model extracted from historical process execution logs (event logs). In practice, different business domains might require different kinds of predictions. Hence, it is important to have a means for properly specifying the desired prediction tasks, and a mechanism to deal with these various prediction tasks. Although there have been many studies in this area, they mostly focus on a specific prediction task. This work introduces a language for specifying the desired prediction tasks, and this language allows us to express various kinds of prediction tasks. This work also presents a mechanism for automatically creating the corresponding prediction model based on the given specification. Differently from previous studies, instead of focusing on a particular prediction task, we present an approach to deal with various prediction tasks based on the given specification of the desired prediction tasks. We also provide an implementation of the approach which is used to conduct experiments using real-life event logs.