Peak Prediction in Time Series: Comparing Approaches for Energy High-Load Prediction

Peaks in time series represent significant events in the measurements of a phenomenon over time, such as a sudden increase in the sales of an item on a specific day of the week. Predicting peaks in advance can support decision-making in various domains. For example, predicting demand for healthcare services helps adjust nurses’ work schedules in a hospital. This paper proposes a time series mining task named peak prediction, from which most applications that manage resources based on expected demand can benefit. We investigate various approaches, such as conventional machine learning and deep learning methods, to build global and individual models for peak prediction in time series. We evaluate these approaches in a load demand problem on smart meter energy consumption. In this task, a model predicts the customer’s maximum daily energy consumption in the following days (7 days ahead) and identifies which day it will occur. Accurate peak demand predictions for the consumers in a region help utility companies make informed decisions regarding capacity planning, load balancing, and integrating renewable energy sources. Since we are interested in two target variables simultaneously (i.e., peak magnitude and peak position), we evaluated whether multi-target approaches improve single-target models’ performance by correlating both targets in the learning process. Our experimental evaluation considers a dataset comprising three years of daily energy consumption from 1,757 customers. The results show the potential of individual machine learning models induced by simple algorithms such as Linear Regression and XGBoost and multi-target methods’ lack of contribution (or even negative impact) for the performance.