Research on Peak Load Shifting for Mixing Procedure in the Tyre Industry

2013 ◽  
Vol 316-317 ◽  
pp. 3-6
Author(s):  
Dian Cai Yang ◽  
Wei Wei Ning ◽  
Kai Wang
Keyword(s):  
Power Consumption ◽  
Electric Power ◽  
Scheduling Algorithm ◽  
Peak Load ◽  
Real Data ◽  
Electricity Price ◽  
Rubber Compound ◽  
Load Shifting ◽  
Planning And Scheduling ◽  
Tyre Industry

Since the establishment of peak-valley electricity price system, many enterprises in China have devoted to research on peak load shifting strategies. This paper firstly analyzes the feasibility of applying peak load shifting for mixing procedure in two aspects: rubber compound standard and electric power consumption, then establishes the shifting model, makes depth description to the planning and scheduling algorithm that implements the model, then verify the model with real data and finally reach the expected goal. At last, the paper introduces MESNAC planning and scheduling solutions which have integrated the shifting model.

Global and Local Clustering-Based Regression Models to Forecast Power Consumption in Buildings

2016 ◽  
pp. 207-234
Author(s):  
Gonzalo Vergara ◽  
Juan J. Carrasco ◽  
Jesus Martínez-Gómez ◽  
Manuel Domínguez ◽  
José A. Gámez ◽  
...  
Keyword(s):  
Machine Learning ◽  
Power Consumption ◽  
Electric Power ◽  
Principal Component ◽  
Real Data ◽  
Machine Learning Techniques ◽  
Electric Power Consumption ◽  
Support Vector ◽  
Learning Machines ◽  
The University

The study of energy efficiency in buildings is an active field of research. Modeling and predicting energy related magnitudes leads to analyze electric power consumption and can achieve economical benefits. In this study, classical time series analysis and machine learning techniques, introducing clustering in some models, are applied to predict active power in buildings. The real data acquired corresponds to time, environmental and electrical data of 30 buildings belonging to the University of León (Spain). Firstly, we segmented buildings in terms of their energy consumption using principal component analysis. Afterwards, we applied state of the art machine learning methods and compare between them. Finally, we predicted daily electric power consumption profiles and compare them with actual data for different buildings. Our analysis shows that multilayer perceptrons have the lowest error followed by support vector regression and clustered extreme learning machines. We also analyze daily load profiles on weekdays and weekends for different buildings.

Research on Power Consumption Model Directing at the Intelligent Residential Quarters

2012 ◽  
Vol 614-615 ◽  
pp. 1641-1645
Author(s):  
Yang Shao ◽  
Huai Liang Li
Keyword(s):  
Power Consumption ◽  
Comprehensive Evaluation ◽  
Peak Load ◽  
Evaluation Index ◽  
Load Shifting ◽  
Scientific Strategy ◽  
Consumption Model ◽  
Master Station ◽  
Residential Quarters

According to the intelligent residential quarters, the power consumption model is put forward to supply scientific strategy of the power consumption and influence the users’ habits actively so as to conserve energy and peak load shifting. Using this model we design the intelligent power consumption master station and establish a comprehensive evaluation index to inspect the operating effect of this model.

scholarly journals Integrated Optimization Method of IPPS under TOU and Tiered Electricity Price

2021 ◽  
Vol 2021 ◽  
pp. 1-11
Author(s):  
Erbao Xu ◽  
Yan Li ◽  
Mingshun Yang ◽  
Zhenyu Wang ◽  
Yirou Liu ◽  
...  
Keyword(s):  
Power Consumption ◽  
Energy Saving ◽  
Maximum Load ◽  
Optimization Method ◽  
Electricity Price ◽  
Decision Makers ◽  
Pareto Solution ◽  
Planning And Scheduling ◽  
Manufacturing Enterprises ◽  
Elite Strategy

Energy-saving production is one of the issues that must be paid attention to by today’s manufacturing enterprises. Aiming at the problem of integrated process planning and scheduling (IPPS) in the manufacturing process, considering Time-of-Use (TOU) and tiered electricity price, this paper systematically studies the energy-saving scheduling problem in order to reduce the power consumption in processing and production. To establish the multiobjective optimization mathematical model of the problem, the load balancing problem of the equipment is considered, the minimization of the power consumption and the maximum load of the equipment are taken as the optimization objectives. Then, considering the constraints of resource and multiprocess, the switching strategy of the equipment in idle time is introduced, including shutdown and restart operations. In order to solve the model easily, a multiobjective firefly algorithm (MOFA) based on five-layer coding is designed, and the elite strategy is introduced to protect the excellent firefly individuals in the iterative population. Finally, through a specific example, the Pareto solution set is obtained, which provides a reference scheme for decision-makers, and verifies the correctness of the model and the effectiveness of the algorithm.

Global and Local Clustering-Based Regression Models to Forecast Power Consumption in Buildings

2019 ◽  
pp. 506-536
Author(s):  
Gonzalo Vergara ◽  
Juan J. Carrasco ◽  
Jesus Martínez-Gómez ◽  
Manuel Domínguez ◽  
José A. Gámez ◽  
...  
Keyword(s):  
Machine Learning ◽  
Power Consumption ◽  
Electric Power ◽  
Principal Component ◽  
Real Data ◽  
Machine Learning Techniques ◽  
Electric Power Consumption ◽  
Support Vector ◽  
Learning Machines ◽  
The University

The study of energy efficiency in buildings is an active field of research. Modeling and predicting energy related magnitudes leads to analyze electric power consumption and can achieve economical benefits. In this study, classical time series analysis and machine learning techniques, introducing clustering in some models, are applied to predict active power in buildings. The real data acquired corresponds to time, environmental and electrical data of 30 buildings belonging to the University of León (Spain). Firstly, we segmented buildings in terms of their energy consumption using principal component analysis. Afterwards, we applied state of the art machine learning methods and compare between them. Finally, we predicted daily electric power consumption profiles and compare them with actual data for different buildings. Our analysis shows that multilayer perceptrons have the lowest error followed by support vector regression and clustered extreme learning machines. We also analyze daily load profiles on weekdays and weekends for different buildings.

Software including the functions of automated analysis of electric power consumption in pipeline oil transportation

2011 ◽  
Vol 8 (1) ◽  
pp. 233-238
Author(s):  
R.M. Bogdanov ◽  
S.V. Lukin
Keyword(s):  
Power Consumption ◽  
Electric Power ◽  
Data Model ◽  
Automated Analysis ◽  
Petroleum Products ◽  
Electric Power Consumption ◽  
Scientific Center ◽  
Relational Data Model ◽  
Oil Transportation ◽  
Academy Of Sciences

Oil and petroleum products transportation is characterized by a significant cost of electric power. Correct oil and petroleum products accounting and forecasting requires knowledge of many factors. The software for norms of electric power consumption analysis for the planned period was developed at the Ufa Scientific Center of the Russian Academy of Sciences. Based on the principles of the relational data model, a schematic diagram/arrangement for the main oil transportation objects was developed, which allows to hold the initial data and calculated parameters in a structured manner.

scholarly journals Uncontrolled Electric Vehicle Charging Impacts on Distribution Electric Power Systems with Primarily Residential, Commercial or Industrial Loads

Energies ◽  
2021 ◽  
Vol 14 (6) ◽  
pp. 1688 ◽  
Author(s):  
C. Birk Jones ◽  
Matthew Lave ◽  
William Vining ◽  
Brooke Marshall Garcia
Keyword(s):  
Power Systems ◽  
Electric Power ◽  
Low Voltage ◽  
Peak Load ◽  
Electric Power Systems ◽  
Primary System ◽  
Maximum Increase ◽  
Actual Distribution ◽  
The Impact ◽  
Ev Charging

An increase in Electric Vehicles (EV) will result in higher demands on the distribution electric power systems (EPS) which may result in thermal line overloading and low voltage violations. To understand the impact, this work simulates two EV charging scenarios (home- and work-dominant) under potential 2030 EV adoption levels on 10 actual distribution feeders that support residential, commercial, and industrial loads. The simulations include actual driving patterns of existing (non-EV) vehicles taken from global positioning system (GPS) data. The GPS driving behaviors, which explain the spatial and temporal EV charging demands, provide information on each vehicles travel distance, dwell locations, and dwell durations. Then, the EPS simulations incorporate the EV charging demands to calculate the power flow across the feeder. Simulation results show that voltage impacts are modest (less than 0.01 p.u.), likely due to robust feeder designs and the models only represent the high-voltage (“primary”) system components. Line loading impacts are more noticeable, with a maximum increase of about 15%. Additionally, the feeder peak load times experience a slight shift for residential and mixed feeders (≈1 h), not at all for the industrial, and 8 h for the commercial feeder.

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