Abstract
Leveraging smart metering solutions to support energy efficiency on the individual household level poses novel research challenges in monitoring usage and providing accurate load forecasting. Forecasting electricity usage is an especially important component that can provide intelligence to smart meters. In this paper, we propose an enhanced approach for load forecasting at the household level. The impacts of residents’ daily activities and appliance usages on the power consumption of the entire household are incorporated to improve the accuracy of the forecasting model. The contributions of this paper are threefold: (1) we addressed short-term electricity load forecasting for 24 hours ahead, not on the aggregate but on the individual household level, which fits into the Residential Power Load Forecasting (RPLF) methods; (2) for the forecasting, we utilized a household specific dataset of behaviors that influence power consumption, which was derived using segmentation and sequence mining algorithms; and (3) an extensive load forecasting study using different forecasting algorithms enhanced by the household activity patterns was undertaken.
Highlights
Introduction and problem statementThroughout the EU, there has been considerable interest in smarter electricity networks, where increased control over electricity supply and consumption is going to be achieved thanks to investments and improvements in new technologies such as Advanced Metering Infrastructure (AMI)
The contributions of this paper are threefold: (1) we addressed short-term electricity load forecasting for 24 hours ahead, not on the aggregate but on the individual household level, which fits into the Residential Power Load Forecasting (RPLF) methods; (2) for the forecasting, we utilized a household specific dataset of behaviors that influence power consumption, which was derived using segmentation and sequence mining algorithms; and (3) an extensive load forecasting study using different forecasting algorithms enhanced by the household activity patterns was undertaken
The neural network outperformed the other methods in terms of Mean Absolute Percentage Error (MAPE)
Summary
Throughout the EU, there has been considerable interest in smarter electricity networks, where increased control over electricity supply and consumption is going to be achieved thanks to investments and improvements in new technologies such as Advanced Metering Infrastructure (AMI). Smart metering is part of this movement, and it is perceived as a necessary step to achieving EU energy policy goals by the year 2020, that is, to cut greenhouse gas emissions by 20%, to improve energy efficiency by 20% and to ensure that 20% of EU energy demand is supplied by renewable energy sources. Smart metering systems are a part of micro-grid which includes a variety of operational and energy measures including smart appliances, renewable energy resources and energy efficient resources. Attention is paid to smart grid vision and smart homes that can optimize energy consumption and lower electricity bills.
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