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首页> 外文会议>Mediterranean Conference on Power Generation, Transmission, Distribution and Energy Conversion >Electricity Consumption Prognosis with the Combination of Smart Metering and Artificial Neural Networks
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Electricity Consumption Prognosis with the Combination of Smart Metering and Artificial Neural Networks

机译：用智能计量和人工神经网络的组合电力消耗预后

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摘要

This work is an effort in order to predict in house sector one hour ahead the electricity consumption (EC). For this purpose, the combination of a Smart Meter (SM) with Automated Meter reading technology (AMR), online meteorological data and Artificial Neural Network (ANN) models were used in an area of Athens city, Greece. Concretely, a SM was used to record the EC in a residence in the Moschato municipality, which is located in the south of Athens city. Simultaneously, through the web portal Metar which is under the auspices National Observatory of Athens, online meteorological data concerning the area of Moschato were collected. Finally, an ANN forecasting model was developed and applied in order to predict the energy demand in a residence house, one hour ahead. Results showed that the combination of a SM and ANN model is a very promising tool for better management of electricity demand in the future.
机译：这项工作是一种努力，以便在电力消耗（EC）提前一小时内预测房屋部门。为此目的，智能电表（SM）与自动抄表技术（AMR），在线气象数据和人工神经网络（ANN）模型的组合用于希腊雅典市的一个地区。具体地，SM用于在MoSchato市的住所中记录EC，位于雅典市中心。同时，通过受雅典国家天文台的网络门户标准，收集了关于Moschato领域的网上气象数据。最后，开发和应用了ANN预测模型，以预测住宅房屋的能源需求，提前一小时。结果表明，SM和ANN模型的组合是一个非常有前途的工具，可在未来更好地管理电力需求。

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