Peak Shaving Impact on Load Forecasting: A Strategy for Mitigation

This paper introduces a novel approach for improving load forecasting accuracy in smart grids when integrating peak shaving strategies. Our research proposes a practical framework that demonstrates the negative impact of peak shaving on load forecasting accuracy and identifies the necessary algorithmic transformations to accommodate load demand fluctuations arising from such actions. We integrate a binary descriptor carrying information about the timing of the peak shaving events into the input vector used to train the forecasting model. We conducted two investigations based on two different billing strategies: "daily peak shaving" to address variable power rates and "peak shaving on a subset of days within a monthly billing cycle" to address power rates based on energy/peak demand. Our approach was validated using two public datasets from different climates collected in Australia and Greece. It evaluated two different forecasting techniques, the Feedforward Neural Network and the Long Short-Term Memory Network, with different levels and frequencies of peak shaving. The results demonstrate that our solution effectively mitigates the negative impact of peak shaving, leading to significant forecasting accuracy enhancements across both forecasting techniques, billing strategies, and peak shaving frequency and levels. The solution remarkably reduced the forecasting error metrics in all cases studied when comparing forecasts generated with and without the peak shaving descriptor. This study also provides practical insights into how our approach can be applied in real-world power systems to not only improve load forecasting accuracy but also assess the impact of demand response actions and improve grid reliability and efficiency.