Analyzing the impact of electricity price forecasting on energy cost-aware scheduling

• We assess how various price forecast properties affect energy aware scheduling cost.
• We introduce a forecasting model, co-optimizing standard and non-standard metrics.
• We show that incorporating the Spearman Rank correlation can improve performance.
• We show that the proposed forecast leads to significant cost savings.

Energy cost-aware scheduling, i.e., scheduling that adapts to real-time energy price volatility, can save large energy consumers millions of dollars every year in electricity costs. Energy price forecasting coupled with energy price-aware scheduling, is a step toward this goal. In this work, we study cost-aware schedules and the effect of various price forecasting schemes on the end schedule-cost. We show that simply optimizing price forecasts based on classical regression error metrics (e.g., Mean Squared Error), does not work well for scheduling. Price forecasts that do result in significantly better schedules, optimize a combination of metrics, each having a different impact on the end-schedule-cost. For example, both price estimation and price ranking are important for scheduling, but they carry different weight. We consider day-ahead energy price forecasting using the Irish Single Electricity Market as a case-study, and test our price forecasts for two real-world scheduling applications: animal feed manufacturing and home energy management systems. We show that price forecasts that co-optimize price estimation and price ranking, result in significant energy-cost savings. We believe our results are relevant for many real-life scheduling applications that are currently plagued with very large energy bills.