Short Term Peak Timing Forecasting

Peak load forecasting is crucial for reliable and effective grid operation. The day-to-day operation of the power grid requires load scheduling and dispatches of different energy resources including Energy Storage System and Demand Side Management programs. An effective implementation of these peak-shaving strategies relies heavily on when the peak demand occurs. Hence, forecasting the timing of peak load is as important as forecasting its magnitude.
A review of relevant literature indicates that there is no inclusive study on the topic of peak timing forecasting. This research aims to bridge the gap between industry requirements and academic research by addressing some key questions. First, the study defines the different forms of peak timing problems that arise in grid operation. Next, we investigate the problem of how we measure the peak timing forecast errors. The research critically reviews error measures used in the literature for peak timing forecasting. Based on the findings five new application-specific error measures are proposed. The research then focuses on one of the manifestations of the peak timing problem, that is, forecasting daily peak hours.
We analyzed the accuracy of peak hour forecasts from a state-of-the-art hourly load forecasting model and set it as the benchmark. The model selection process using different peak timing errors and load shape errors is investigated. Furthermore, two different frameworks for peak hour forecasting have been developed. The effectiveness of the proposed frameworks is empirically demonstrated in two case studies. The first case study is from a medium-sized Utility in the U.S. and the second one is from ISO New England. The proposed models demonstrate improved forecast results on the benchmark model by 12-16% in the test years of the two case studies. Additionally, when the models are only evaluated on the critical days with very high demands, they outperform the benchmark by 25-53%. Findings from this study emphasize the importance of developing explicit models for peak hour forecasting by analyzing the key determinants that vary with geographical location and regional factors.