Abstract
As new grid edge technologies emerge—such as rooftop solar panels, battery storage, and controllable water heaters—quantifying the uncertainties of building load forecasts is becoming more critical. The recent adoption of smart meter infrastructures provided new granular data streams, largely unavailable just ten years ago, that can be utilized to better forecast building-level demand. This paper uses Bayesian Structural Time Series for probabilistic load forecasting at the residential building level to capture uncertainties in forecasting. We use sub-hourly electrical submeter data from 120 residential apartments in Singapore that were part of a behavioral intervention study. The proposed model addresses several fundamental limitations through its flexibility to handle univariate and multivariate scenarios, perform feature selection, and include either static or dynamic effects, as well as its inherent applicability for measurement and verification. We highlight the benefits of this process in three main application areas: (1) Probabilistic Load Forecasting for Apartment-Level Hourly Loads; (2) Submeter Load Forecasting and Segmentation; (3) Measurement and Verification for Behavioral Demand Response. Results show the model achieves a similar performance to ARIMA, another popular time series model, when predicting individual apartment loads, and superior performance when predicting aggregate loads. Furthermore, we show that the model robustly captures uncertainties in the forecasts while providing interpretable results, indicating the importance of, for example, temperature data in its predictions. Finally, our estimates for a behavioral demand response program indicate that it achieved energy savings; however, the confidence interval provided by the probabilistic model is wide. Overall, this probabilistic forecasting model accurately measures uncertainties in forecasts and provides interpretable results that can support building managers and policymakers with the goal of reducing energy use.
Highlights
We examine its ability to be used as a measurement and verification (M&V) tool to measure the savings effect of three demand response programs administered to the same residential units
After building the Bayesian Structural Time Series (BSTS) and autoregressive integrated moving average (ARIMA) model on the 49 aggregated apartment units, we found a mean absolute percentage error (MAPE) of 0.127 for the BSTS model compared to a 0.206 error for the ARIMA model, meaning the BSTS model has better performance
We use submeter data produced from the meters to improve the forecasting accuracy of the BSTS model
Summary
Unlike fossil-fuel-based generation, grid managers cannot control when these resources supply energy to the grid. Instead, their output is dependent on the weather, generating energy when the sun shines or the wind blows. As renewables grow and the infrastructure of poles and wires that carry electricity age, the grid manager’s job to meet demand with supply will become more difficult. Compounding this problem, the demand-side of the electricity grid is quickly changing. Predicting future load and characterizing its uncertainty is critical to maintaining this delicate balance and ensuring the reliability of the whole system [4]
Sign-in/Register to view highlights & summary Talk to us
Join us for a 30 min session where you can share your feedback and ask us any queries you have
Schedule a callDisclaimer: All third-party content on this website/platform is and will remain the property of their respective owners and is provided on "as is" basis without any warranties, express or implied. Use of third-party content does not indicate any affiliation, sponsorship with or endorsement by them. Any references to third-party content is to identify the corresponding services and shall be considered fair use under The CopyrightLaw.
