scholarly journals An Electric Bus Power Consumption Model and Optimization of Charging Scheduling Concerning Multi-External Factors

Energies ◽  
2018 ◽  
Vol 11 (8) ◽  
pp. 2060 ◽  
Author(s):  
Yajing Gao ◽  
Shixiao Guo ◽  
Jiafeng Ren ◽  
Zheng Zhao ◽  
Ali Ehsan ◽  
...  
Keyword(s):  
Power Consumption ◽  
Prediction Model ◽  
Large Scale ◽  
Operating Cost ◽  
Electricity Consumption ◽  
External Factors ◽  
Electric Bus ◽  
Consumption Data ◽  
Power Consumption Prediction ◽  
Consumption Prediction

With the large scale operation of electric buses (EBs), the arrangement of their charging optimization will have a significant impact on the operation and dispatch of EBs as well as the charging costs of EB companies. Thus, an accurate grasp of how external factors, such as the weather and policy, affect the electric consumption is of great importance. Especially in recent years, haze is becoming increasingly serious in some areas, which has a prominent impact on driving conditions and resident travel modes. Firstly, the grey relational analysis (GRA) method is used to analyze the various external factors that affect the power consumption of EBs, then a characteristic library of EBs concerning similar days is established. Then, the wavelet neural network (WNN) is used to train the power consumption factors together with power consumption data in the feature library, to establish the power consumption prediction model with multiple factors. In addition, the optimal charging model of EBs is put forward, and the reasonable charging time for the EB is used to achieve the minimum operating cost of the EB company. Finally, taking the electricity consumption data of EBs in Baoding and the data of relevant factors as an example, the power consumption prediction model and the charging optimization model of the EB are verified, which provides an important reference for the optimal charging of the EB, the trip arrangement of the EB, and the maximum profit of the electric public buses.

scholarly journals Short-Term Electricity Consumption Forecasting Based on the EMD-Fbprophet-LSTM Method

2021 ◽  
Vol 2021 ◽  
pp. 1-9
Author(s):  
Guorong Zhu ◽  
Sha Peng ◽  
Yongchang Lao ◽  
Qichao Su ◽  
Qiujie Sun
Keyword(s):  
Time Series ◽  
Power Consumption ◽  
Prediction Model ◽  
Time Series Prediction ◽  
Electricity Consumption ◽  
Operating Efficiency ◽  
Short Term ◽  
Model Based ◽  
Consumption Data ◽  
Consumption Prediction

Short-term electricity consumption data reflects the operating efficiency of grid companies, and accurate forecasting of electricity consumption helps to achieve refined electricity consumption planning and improve transmission and distribution transportation efficiency. In view of the fact that the power consumption data is nonstationary, nonlinear, and greatly influenced by the season, holidays, and other factors, this paper adopts a time-series prediction model based on the EMD-Fbprophet-LSTM method to make short-term power consumption prediction for an enterprise's daily power consumption data. The EMD model was used to decompose the time series into a multisong intrinsic mode function (IMF) and a residual component, and then the Fbprophet method was used to predict the IMF component. The LSTM model is used to predict the short-term electricity consumption, and finally the prediction value of the combined model is measured based on the weights of the single Fbprophet and LSTM models. Compared with the single time-series prediction model, the time-series prediction model based on the EMD-Fbprophet-LSTM method has higher prediction accuracy and can effectively improve the accuracy of short-term regional electricity consumption prediction.

scholarly journals A Non-Intrusive, Traffic-Aware Prediction Framework for Power Consumption in Data Center Operations

Energies ◽  
2020 ◽  
Vol 13 (3) ◽  
pp. 663
Author(s):  
Zheng Liu ◽  
Mian Zhang ◽  
Xusheng Zhang ◽  
Yun Li
Keyword(s):  
Power Consumption ◽  
Prediction Model ◽  
Data Center ◽  
Data Centers ◽  
Prediction Performance ◽  
Coarse Grained ◽  
Fine Grained ◽  
Encoding Strategy ◽  
Power Consumption Prediction ◽  
Consumption Prediction

Modern cloud computing relies heavily on data centers, which usually host tens of thousands of servers. Predicting the power consumption accurately in data center operations is crucial for energy optimization. In this paper, we formulate the power consumption prediction at both the fine-grained and coarse-grained level. We carefully discuss the desired properties of an applicable prediction model and propose a non-intrusive, traffic-aware prediction framework for power consumption. We design a character-level encoding strategy for URIs and employ both convolutional and recurrent neural networks to develop a unified prediction model. We use real datasets to simulate requests and analyze the characteristics of the collected power consumption series. Extensive experiments demonstrate that our proposed framework can achieve superior prediction performance compared to other popular leading prediction methods.

scholarly journals Twin-Shaft Mixers’ Mechanical Behavior Numerical Simulations of the Mix and Phases

Machines ◽  
2019 ◽  
Vol 7 (2) ◽  
pp. 39 ◽  
Author(s):  
Maria Cristina Valigi ◽  
Silvia Logozzo ◽  
Luca Landi ◽  
Claudio Braccesi ◽  
Luca Galletti
Keyword(s):  
Power Consumption ◽  
Prediction Model ◽  
Mechanical Behavior ◽  
Numerical Simulations ◽  
Design Parameters ◽  
Force Estimation ◽  
Mixing Processes ◽  
Increase Productivity ◽  
Power Consumption Prediction ◽  
Consumption Prediction

In this paper, the mechanical behavior of concrete twin-shaft mixers is analyzed in terms of power consumption and exchanged forces between the mixture and the mixing organs during the mixing cycle. The mixing cycle is divided into two macro phases, named transient and regime phase, where the behavior of the mixture is modeled in two different ways. A force estimation and power consumption prediction model are presented for both the studied phases and they are validated by experimental campaigns. From the application of this model to different machines and by varying different design parameters, the optimization of the power consumption of the concrete twin-shaft mixers is analyzed. Results of this work can be used to increase productivity and profitability of concrete mixers and reduce energy waste in the industries which involve mixing processes.

Pre-execution power consumption prediction of computational multithreaded workloads

2014 ◽  
Vol 17 (4) ◽  
pp. 1323-1333 ◽  
Author(s):  
Hamid Fadishei ◽  
Hossein Deldari ◽  
Mahmoud Naghibzadeh
Keyword(s):  
Power Consumption ◽  
Multithreaded Workloads ◽  
Power Consumption Prediction ◽  
Consumption Prediction

scholarly journals Multi-State Energy Classifier to Evaluate the Performance of the NILM Algorithm

Sensors ◽  
2019 ◽  
Vol 19 (23) ◽  
pp. 5236 ◽  
Author(s):  
Sanket Desai ◽  
Rabei Alhadad ◽  
Abdun Mahmood ◽  
Naveen Chilamkurti ◽  
Seungmin Rho
Keyword(s):  
Power Consumption ◽  
Large Scale ◽  
Single Point ◽  
Ground Truth ◽  
Significant Rise ◽  
The State ◽  
Smart Meters ◽  
Energy Estimation ◽  
Consumption Data ◽  
Load Monitoring

With the large-scale deployment of smart meters worldwide, research in non-intrusive load monitoring (NILM) has seen a significant rise due to its dual use of real-time monitoring of end-user appliances and user-centric feedback of power consumption usage. NILM is a technique for estimating the state and the power consumption of an individual appliance in a consumer’s premise using a single point of measurement device such as a smart meter. Although there are several existing NILM techniques, there is no meaningful and accurate metric to evaluate these NILM techniques for multi-state devices such as the fridge, heat pump, etc. In this paper, we demonstrate the inadequacy of the existing metrics and propose a new metric that combines both event classification and energy estimation of an operational state to give a more realistic and accurate evaluation of the performance of the existing NILM techniques. In particular, we use unsupervised clustering techniques to identify the operational states of the device from a labeled dataset to compute a penalty threshold for predictions that are too far away from the ground truth. Our work includes experimental evaluation of the state-of-the-art NILM techniques on widely used datasets of power consumption data measured in a real-world environment.

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