The industrial process consumes substantial energy and emits large amounts of carbon dioxide. With the help of accurate energy consumption and carbon emissions forecasting, industrial enterprises would find it easier to achieve cleaner production, optimize the energy structure, and reduce production costs and carbon emissions by gaining deeper control over the production situation. Due to the oversaturation of machine learning modeling methods, forecasting models face difficulties in improving accuracy and extracting data features. The deep learning method is introduced to address these issues. However, the inaccurate measurement of key parameters and anomalies in data transmission strengthens the uncertainty of industrial big data. This makes the machine learning-based forecasting models show strong uncertainty and poor generalizability. Thus, it is significantly difficult to improve the accuracy of the current industrial energy consumption and carbon emissions forecasting model in different industrial scenarios. This work summarizes the research on energy consumption and carbon emissions forecasting for industrial processes in recent years. Combined with the actual problems of current industrial processes, this work summarizes three kinds of forecasting models: (i) the multi-step forecasting model based on the combination of deep learning and model uncertainty, (ii) the combined mechanism and data-driven method-based forecasting model, and (iii) the intelligent algorithms-based forecasting model. These models could become a new path for establishing generic industrial process energy consumption and carbon emissions forecasting models in the future. 

Energy consumption and carbon emissions forecasting for industrial processes: Status, challenges and perspectives

The energy crisis has forced researchers to look for various non-conventional energy sources. Wind energy is one of the potential sources, and researchers have invested resources in developing different kinds of wind turbines. Vertical axis wind turbines (VAWT) have received less attention than their horizontal-axis counterparts. A helical-bladed VAWT is preferred because it makes perfect sense as an improvement in design, as they have higher azimuth angles of power generation capabilities. This paper studies the effects of the helix angle of blades in the aerodynamic performance of VAWT using 3D numerical simulations. Three different helix angles of 60°, 90°, and 120° of a three-bladed VAWT operating across different tip speed ratios were studied. Turbulence is modelled using a four-equation transition SST k-ω model (shear stress transport). The 60° helical-bladed VAWT was found to be better performing in comparison with all other helical-bladed and straight-bladed VAWT. The ripple effects on the shaft are also analysed using a standard deviation plot of the moment coefficient generated by a single blade over one complete cycle of its rotation. It was observed that the greater the helix angle, the lower the standard deviation. The paper also tries to analyse the percentage of power generated by each quartile of flow and the contribution of each section of the blade. Ansys FLUENT was employed for the entire study. A comparative study between different helical-bladed VAWT and straight-bladed VAWT was carried out along with wake structure analysis and flow contours for a better understanding of the flow field.

Effect of Helix Angle on the Performance of Helical Vertical Axis Wind Turbine

A Data-Driven Deterministic Forecast-Based Probabilistic Method for Uncertainty Estimation of Wind Power Generation

The widespread attention in the growth of clean energy for electricity production necessitates an accurate and reliable generation and demand forecasts. However, the decision-making process in electric power industry involves more uncertainty due to the transition towards distributed energy systems, which are not addressed in the conventional point forecasts. This paper proposes a probabilistic method termed as Deep Quantile Regression (DQR) for the construction of prediction intervals (PIs) that can potentially quantify uncertainty in the point forecasts of wind power generation and demand. The effectiveness of DQR is examined using the low and high seasonal wind and demand datasets. PIs with various confidence levels of 99%, 95% and 90% are estimated by constructing the appropriate quantiles using the proposed DQR method. The quantitative comparison of the quality in all the estimated PIs using the proposed method proves to outperform the other state-of-the-art methods.

Deep Quantile Regression Based Wind Generation and Demand Forecasts

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Deep Quantile Regression Based Wind Generation and Demand Forecasts