Estimating the electricity load is a crucial task in the planning of power generation systems and the efficient operation and sustainable growth of modern electricity supply networks. Especially with the advent of smart grids, the need for fairly precise and highly reliable estimation of electricity load is greater than ever. It is a challenging task to estimate the electricity load with high precision. Many energy demand management methods are used to estimate future energy demands correctly. Machine learning methods are well adapted to the nature of the electrical load, as they can model complicated nonlinear connections through a learning process containing historical data patterns. Many scientists have used machine learning (ML) to anticipate failure before it occurs as well as predict the outcome. ML is an artificial intelligence (AI) subdomain that involves studying and developing mathematical algorithms to understand data or obtain data directly without relying on a prearranged model algorithm. ML is applied in all industries. In this paper, machine learning strategies including artificial neural network (ANN), multiple linear regression (MLR), adaptive neuro-fuzzy inference system (ANFIS), and support vector machine (SVM) were used to estimate electricity demand and propose criteria for power generation in Cyprus. The simulations were adapted to real historical data explaining the electricity usage in 2016 and 2107 with long-term and short-term analysis. It was observed that electricity load is a result of temperature, humidity, solar irradiation, population, gross national income (GNI) per capita, and the electricity price per kilowatt-hour, which provide input parameters for the ML algorithms. Using electricity load data from Cyprus, the performance of the ML algorithms was thoroughly evaluated. The results of long-term and short-term studies show that SVM and ANN are comparatively superior to other ML methods, providing more reliable and precise outcomes in terms of fewer estimation errors for Cyprus’s time series forecasting criteria for power generation.
Summary Lithium‐ion batteries are among the most commonly used batteries to produce power for electric vehicles, which leads to the higher needs for battery thermal management system (BTMS). There are many key concerning points for the users of these batteries, which include reliability, safety, life cycle, and the operating temperature of the batteries. It is known through review that water is the best coolant for batteries, in which the maximum temperature was 43.3°C while the temperature of the coolant was 30°C during the discharge rate of battery pack at 4 C. An effective cooling system is necessary in prolonging the battery life, which controls the temperature difference between the batteries and the peak temperature of the battery. This review paper aims to summarize the recent published papers on battery liquid‐cooling systems, which include: battery pack design, liquid‐cooling system classification, and coolant performance. Furthermore, this study discusses other factors related to the recent studies, such as the properties and applications of different liquid coolants (oil and water) under the classification of liquid‐cooling system and the difference between passive and active, indirect and direct, and external and internal cooling systems are discussed. Moreover, this paper investigates the effect of temperature on the performance of battery in three aspects: low, high, and differential temperatures. Moreover, the study provides a systematic review of liquid‐based systems for direct and indirect contact modes.
The Friction stir welding (FSW) is recently presented so to join different materials without the melting process as a solid-state joining technique. A widely application for the FSW process is recently developed in automotive industries. To create the welded components by using the FSW, the plunged probe and shoulder as welding tools are used. The Finite Element Method (FEM) can be used so to simulate and analyze material flow during the FSW process. As a result, thermal and mechanical stresses on the workpiece and welding tool can be analyzed and decreased. Effects of the welding process parameters such as tool rotational speed, welding speed, tool tilt angle, depth of the welding tool, and tool shoulder diameter can be analyzed and optimized so to increase the efficiency of the production process. Material characteristics of welded parts such as hardness or grain size can be analyzed so to increase the quality of part production. Residual stress, strain, deformation, and estimations of the temperatures in the welding area can be predicted using the simulation of FSW in the FEM software. Heat generation, thermal, and thermomechanical analyses can also be implemented on the welded parts to analyze the distribution of temperature and strain in the heat-affected zone (HAZ). Moreover, welding operations of dissimilar metals can be analyzed using numerical simulation to increase the capabilities of the welding methodology in different industrial applications. In this article, a review of the FSW process is presented. As a result, the research filed can be moved forward by reviewing and analyzing recent achievements in the published papers. This article is part of a Special Issue on Lightweighting Materials for Automotive Applications.
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