Automatic generation of saturation constraints and performance expressions for geometric programming based analog circuit sizing

Production equipment such as machines have crucial impact on the overall performance of production operations in manufacturing industries, since there is a strong correlation between the machines and working conditions and performance on the shop floor. Well designed production equipment has the potential to achieve economic gain by reducing the disturbances during the operational phase, to fulfill environmental commitment by reducing emissions and resources consumption and utility, and to increase employee satisfaction ensuring safety and good ergonomics. Therefore, when acquiring production equipment it is important to consider different sustainability aspects relevant to its usage during the operational phase. This study aims at exploring the critical features of production equipment to facilitate different practices in the context of sustainable production operational system, and how manufacturing companies are considering sustainability aspects when acquiring production equipment. The data has been collected based on a literature study, interviews conducted in different manufacturing companies located in Sweden, attending group discussion sessions, and reviewing machines’ technical regulation guidelines. Some of the critical features identified are error proofing, setup time, one-piece flow, automatic generation of required data, reduction of energy and resource consumption, together with worker’s health and safety, etc. The data indicates that companies specify different features of machines based on the requirements of operational performance and these features are aligned with different lean techniques, green practice, and safety issues. However, during acquisition process of production equipment the environmental issues are still not prioritized yet compared to lean and safety aspects. Budget constraint, insufficient information of the whole life cycle costing and lack of innovation from the equipment suppliersÂť side are exampled of major barriers for acquiring more environment-friendly production equipment.

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ANN-Based Day-Ahead Short Term Load Forecasting

10.32920/ryerson.14656170 ◽

2021 ◽

Author(s):

Heng Zhao

Keyword(s):

Power Systems ◽

Power System ◽

Meteorological Data ◽

Load Forecasting ◽

Automatic Generation ◽

Training Algorithms ◽

Security Assessment ◽

Short Term ◽

Short Term Load Forecasting ◽

And Performance

Load forecasting (LF) is of great significance for effective operation, utilization, safety and reliability of the modern electric power systems. Load forecasting can be categorized into very short term, short-term, medium-term, and long-term forecasts, depending on which time scale is concerned. The short term load forecasting (STLF) plays an increasingly important role in achieving a more efficient, reliable and safe power system. Its outputs are the indispensable inputs of generating scheduling, power system security assessment and power dispatch. In the era of smart grid (SG), STLF is the basic building block to imply Demand Side Management (DSM) in areas such as automatic generation control, load estimation, energy purchasing, and contract evaluation, etc. The accuracy of STLF is of essential importance for both economic and reliability. In the last few decades, various methods have been devised and applied to perform STLF. Due to its superior capability of handling the nonlinearity, Artificial Intelligence (AI) based techniques are gaining more popularity in a variety of applications. The objective of this study is to review, categorize, evaluate, and analyze the principle, application, and performance of STLF techniques. It builds up several feed forward Artificial Neural Networks (ANN) models with different configurations, and studies the mechanism of ANN for effective STLF. With 12 years of hourly load and meteorological data sets of a section of the City of Toronto, the configurations are built up with different hidden layers, activating function, training algorithms and both un-normalized and normalized data to predict the day ahead STLF with satisfactory result achieved.

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