AI Summary & Key Takeaways
AI InsightThe results show that better performance can be achieved using the CwCC approach for each of the three forecast methods, and LSTM outperforms other methods in each scenario.
📌 3 Key Takeaways
Abstract
Accurate aggregate (total) short-term load forecasting of Smart Homes (SHs) is essential in planning and management of power utilities. The baseline approach consists of simply designing and training predictors for the aggregated consumption data. Nevertheless, better performance can be achieved by using a clustering-based forecasting strategy. In such strategy, the SHs are grouped according to some metric and the forecast of each group's total consumption are summed to reach the forecast of aggregate consumption of all SHs. Although the idea is simple, its implementation requires fine-detailed steps. This paper proposes a novel clustering-based aggregate-level forecast framework, so called Clusters with Competing Configurations (CwCC) approach and then compares its performance to the baseline strategy, namely Clusters with the Same Configurations (CwSC) approach. The Configurations in the name refers to the configurations of ARIMA, Multi-Layer Perceptron (MLP), and Long Short-Term Memory (LSTM) forecasting methods, which the CwCC approach uses. We test the CwCC approach on Smart Grid Smart City Dataset. The results show that better performance can be achieved using the CwCC approach for each of the three forecast methods, and LSTM outperforms other methods in each scenario.