Abstract
We propose M-SRPCNN, a fully convolutional generative deep neural network to recover missing historical hourly data from a sensor based on the historic monthly energy consumption. The network performs a reconstruction of the load profile while keeping the overall monthly consumption, which makes it suitable to effectively replace energy apportioning systems. Experiments demonstrate that M-SRPCNN can effectively reconstruct load curves from single month overall values, outperforming traditional apportioning systems.
Highlights
Before the arrival of smart meters, it was common for Energy Management Systems (EMS) to store energy consumption information in a monthly resolution
We propose to apply a fully Convolutional Neural Network to solve the problem, which we named M-SRPCNN
Since historic data is more likely to exist in a monthly form, in this paper, we present a novel approach to address the restoration of a load profile in hourly resolution from a load profile in monthly resolution by modelling the problem in a deep neural network, demonstrating that it is possible to estimate a general hourly load profiles based on the month values
Summary
Before the arrival of smart meters, it was common for Energy Management Systems (EMS) to store energy consumption information in a monthly resolution. This is the de-facto scenario in historical old data or under scenarios where only the monthly consumption invoice data are available. The problem of expanding a single value into several values is known as a superresolution (SR) problem, which was first proposed for images [1]. This idea was applied for energy consumptions to increase the frequency of the measurements.
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