Abstract
By virtue of the steady societal shift to the use of smart technologies built on the increasingly popular smart grid framework, we have noticed an increase in the need to analyze household electricity consumption at the individual level. In order to work efficiently, these technologies rely on load forecasting to optimize operations that are related to energy consumption (such as household appliance scheduling). This paper proposes a novel load forecasting method that utilizes a clustering step prior to the forecasting step to group together days that exhibit similar energy consumption patterns. Following that, we attempt to classify new days into pre-generated clusters by making use of the available context information (day of the week, month, predicted weather). Finally, using available historical data (with regard to energy consumption) alongside meteorological and temporal variables, we train a CNN-LSTM model on a per-cluster basis that specializes in forecasting based on the energy profiles present within each cluster. This method leads to improvements in forecasting performance (upwards of a 10% increase in mean absolute percentage error scores) and provides us with the added benefit of being able to easily highlight and extract information that allows us to identify which external variables have an effect on the energy consumption of any individual household.
Highlights
Over the years, our reliance on electrical appliances has been slowly increasing
We showed that the application of a clustering step that utilizes dimensionality reduction techniques, such as t-SNE, and hierarchical density-based clustering in the form of HDBSCAN leads to significant improvements in forecasting accuracy when taking individual households into consideration
The practicality of the model lies in the availability of the data that it requires to function, primarily with respect to historical energy consumption data for the individual households in question and meteorological data, which can be obtained from numerous sources
Summary
Our reliance on electrical appliances has been slowly increasing. As our dependence on electrical appliances has increased, so too has our consumption of energy [1]and, subsequently, our need for more sophisticated and advanced solutions that can accommodate this growth. The resulting growth in the prevalence of smart grids has given us the opportunity to both control and monitor the energy consumption of individual households on a realtime basis [3], and, through the utilization of applications built using this framework, we are capable of achieving an overall reduction in the amount of energy that we, as the human race, consume. This opens up the possibility to alleviate some of the inherent risks associated with the growth in energy consumption, whether that be our overall environmental footprint on the planet or, on a much smaller scale, the financial impact on both suppliers as well as consumers due to instabilities present in current, outdated power grid systems [4].
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