2020 International Conference on Smart Energy Systems and Technologies (SEST)最新文献

The Importance of Predictor Variables and Feature Selection in Day-ahead Electricity Price Forecasting 预测变量及特征选择在日前电价预测中的重要性

2020 International Conference on Smart Energy Systems and Technologies (SEST) Pub Date : 2020-09-01 DOI: 10.1109/SEST48500.2020.9203273

Lennard Visser, T. Alskaif, W. V. van Sark

{"title":"The Importance of Predictor Variables and Feature Selection in Day-ahead Electricity Price Forecasting","authors":"Lennard Visser, T. Alskaif, W. V. van Sark","doi":"10.1109/SEST48500.2020.9203273","DOIUrl":"https://doi.org/10.1109/SEST48500.2020.9203273","url":null,"abstract":"Electricity spot market prices are increasingly affected by an expanding amount of renewables and a growing number of market participants. In an attempt to improve forecasting accuracy, this paper evaluates the importance of 62 predictor variables to forecast the the day-ahead electricity price. These variables describe the electricity price, load, generation and weather at different times in the Netherlands, Belgium and Germany. In this study we assess the performance of four machine learning models that forecast the electricity price. Next, we rank the variables according to their importance and identify to what extent different estimators and feature selection methods affect the performance of the forecasting models. We found that Random Forest regression is the best performing model regardless of the number of features selected and the feature selection method applied. Secondly, the performance of all models was not found to improve significantly after the selection of the top 15 ranked variables. Interestingly the top ranked variables differ significantly per selection method. Moreover, the feature selection methods based on Multi-variate Linear Regression and linear kernel Support Vector Machine were found to give the best performance for all models.","PeriodicalId":302157,"journal":{"name":"2020 International Conference on Smart Energy Systems and Technologies (SEST)","volume":"17 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2020-09-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"114903276","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

引用次数: 6

A Clustering Framework for Residential Electric Demand Profiles 住宅用电需求分布的聚类框架

2020 International Conference on Smart Energy Systems and Technologies (SEST) Pub Date : 2020-09-01 DOI: 10.1109/SEST48500.2020.9203534

Mayank Jain, T. Alskaif, Soumyabrata Dev

引用次数: 8

Applying Neural Networks to Large-Scale Distribution System Analysis: an Empirical Computational Perspective 神经网络在大规模配电系统分析中的应用:经验计算视角

2020 International Conference on Smart Energy Systems and Technologies (SEST) Pub Date : 2020-09-01 DOI: 10.1109/SEST48500.2020.9203347

S. Doumen, R. Bernards, J. Morren, N. Paterakis

{"title":"Applying Neural Networks to Large-Scale Distribution System Analysis: an Empirical Computational Perspective","authors":"S. Doumen, R. Bernards, J. Morren, N. Paterakis","doi":"10.1109/SEST48500.2020.9203347","DOIUrl":"https://doi.org/10.1109/SEST48500.2020.9203347","url":null,"abstract":"Research has shown that Neural Networks (NNs) are capable of accurate and quick low voltage (LV) grid analysis. Therefore, NNs could be a viable method for the middle long-term scenario tool (MLT), a tool created and used by Enexis, one of the major distribution system operator (DSO) in the Netherlands, to analyze future scenarios of LV grids. However, the tool analyzes a substantial amount of LV grids, and each would require a NN. This amount of NNs necessitates a single network architecture and training method for all NNs, which can be achieved by knowing hyperparameters beforehand, since determining hyperparameters is computationally costly. This paper estimates how long it would take to train a substantial amount of NNs, determines if hyperparameters are shareable between NNs of similar-sized LV grids and if hyperparameters are predictable based on LV grid sizes. The results of hyper-parameter sharing show comparable performance between NNs, however, differences start to occur for larger LV grids. Predicting hyperparameters based on LV grid size gives an unsatisfactory performance.","PeriodicalId":302157,"journal":{"name":"2020 International Conference on Smart Energy Systems and Technologies (SEST)","volume":"222 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2020-09-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"130656033","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

引用次数: 0

Online Energy Management of Electric Vehicle Parking-Lots 电动汽车停车场在线能量管理

2020 International Conference on Smart Energy Systems and Technologies (SEST) Pub Date : 2020-09-01 DOI: 10.1109/SEST48500.2020.9203421

Arman Alahyari, David Pozo, Mohammad Ali Sadri

引用次数: 2

A study of the inclusion of vulnerable consumers in energy communities with peer-to-peer exchanges 一项关于将弱势消费者纳入点对点交换能源社区的研究

2020 International Conference on Smart Energy Systems and Technologies (SEST) Pub Date : 2020-09-01 DOI: 10.1109/SEST48500.2020.9203312

Inês F. G. Reis, I. Gonçalves, M. Lopes, C. Henggeler Antunes

引用次数: 2

Deep Reinforcement Learning for Long Term Hydropower Production Scheduling 水电长期生产调度的深度强化学习

2020 International Conference on Smart Energy Systems and Technologies (SEST) Pub Date : 2020-09-01 DOI: 10.1109/SEST48500.2020.9203208

S. Riemer-Sørensen, G. Rosenlund

引用次数: 4

Graph-based Model of Smart Grid Architectures 基于图的智能电网体系结构模型

2020 International Conference on Smart Energy Systems and Technologies (SEST) Pub Date : 2020-09-01 DOI: 10.1109/SEST48500.2020.9203113

Benedikt Klaer, O. Sen, D. Velde, I. Hacker, Michael Andres, Martin Henze

引用次数: 12

Characterization of Aggregated Demand-side Flexibility of Small Consumers 小消费者总需求侧灵活性的表征

2020 International Conference on Smart Energy Systems and Technologies (SEST) Pub Date : 2020-09-01 DOI: 10.1109/SEST48500.2020.9203476

Vahid Rasouli, Á. Gomes, C. H. Antunes

{"title":"Characterization of Aggregated Demand-side Flexibility of Small Consumers","authors":"Vahid Rasouli, Á. Gomes, C. H. Antunes","doi":"10.1109/SEST48500.2020.9203476","DOIUrl":"https://doi.org/10.1109/SEST48500.2020.9203476","url":null,"abstract":"This paper presents a methodology for the characterization of demand-side flexibility of a group of small consumers. The day-ahead time-differentiated price tariffs are considered as the base energy cost to be sent to the consumers by the energy supplier with whom they have a contract. The aggregator, as a third-party agent responsible for the aggregation of demand-side flexibility, defines an hourly reward to motivate consumers to participate in the provision of flexibility by reshaping their consumption. A mixed integer linear programming model is developed including the physically based behavior of demand-side resources consisting of shiftable loads, interruptible loads, thermostatically controlled loads, local generation, battery storage systems and electric vehicles. Each consumer is equipped with a home energy management system that performs the management of end-use loads according to consumer's preferences and restrictions, energy prices and any rewards that may exist. In this study, the potential of consumers to provide flexibility and their response to different reward signals are investigated considering different characteristics of the available energy resources, including load profiles and comfort preferences. Different reward values are defined to assess their impact on the amount of flexibility at the consumer and aggregator levels. Numerical results show the potential of different types of consumers to participate in demand response programs differ as their responsiveness to the rewards is not identical.","PeriodicalId":302157,"journal":{"name":"2020 International Conference on Smart Energy Systems and Technologies (SEST)","volume":"88 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2020-09-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"122022840","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

引用次数: 4

Sliding Mode Control of a Switched Reluctance Motor Drive with Four-Switch Bi-Directional DC-DC Converter for Torque Ripple Minimization 基于四开关双向DC-DC变换器的开关磁阻电机驱动滑模控制以减小转矩脉动

2020 International Conference on Smart Energy Systems and Technologies (SEST) Pub Date : 2020-09-01 DOI: 10.1109/SEST48500.2020.9203523

Ertugrul Ates, B. Tekgun, G. Ablay

引用次数: 1

Impact of the Temporal Distribution of Faults on Prediction of Voltage Anomalies in the Power Grid 故障时间分布对电网电压异常预测的影响

2020 International Conference on Smart Energy Systems and Technologies (SEST) Pub Date : 2020-09-01 DOI: 10.1109/SEST48500.2020.9203569

Torfinn Skarvatun Tyvold, Bendik Nybakk Torsæter, C. Andresen, Volker Hoffmann

{"title":"Impact of the Temporal Distribution of Faults on Prediction of Voltage Anomalies in the Power Grid","authors":"Torfinn Skarvatun Tyvold, Bendik Nybakk Torsæter, C. Andresen, Volker Hoffmann","doi":"10.1109/SEST48500.2020.9203569","DOIUrl":"https://doi.org/10.1109/SEST48500.2020.9203569","url":null,"abstract":"Is it possible to reliably predict voltage anomalies in the power grid minutes in advance using machine learning models trained on large quantities of historical data collected by power quality analysers (PQA)? Very little previous research has been done on this topic. To investigate whether this is possible a machine learning model was developed that attempts to predict voltage anomalies 10 minutes in advance based on the presence of early warning signs in the preceding 50 minutes. The model was trained on voltage data collected from 49 measuring locations in the Norwegian power grid. Although results were inconclusive, it was observed that the time that has passed since the previous fault at the same location is a major factor to consider when estimating the probability that a new fault is imminent. It was observed that the probability that a new fault is imminent is proportional to the logarithm of the time passed since the previous anomaly. This means that the risk of a new anomaly is drastically reduced as more time passes since the previous anomaly. This is important to take into consideration when attempting to develop a model that estimates the probability that a new fault is imminent.","PeriodicalId":302157,"journal":{"name":"2020 International Conference on Smart Energy Systems and Technologies (SEST)","volume":"48 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2020-09-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"130151229","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

引用次数: 3