Methodology for the Quantification of the Impact of Weather Forecasts in Predictive Simulation Models

Abstract

The use of Building Energy Models (BEM) has become widespread to reduce building energy consumption. Projection of the model in the future to know how different consumption strategies can be evaluated is one of the main applications of BEM. Many energy management optimization strategies can be used and, among others, model predictive control (MPC) has become very popular nowadays. When using models for predicting the future, we have to assume certain errors that come from uncertainty parameters. One of these uncertainties is the weather forecast needed to predict the building behavior in the near future. This paper proposes a methodology for quantifying the impact of the error generated by the weather forecast in the building’s indoor climate conditions and energy demand. The objective is to estimate the error introduced by the weather forecast in the load forecasting to have more precise predicted data. The methodology employed site-specific, near-future forecast weather data obtained through online open access Application Programming Interfaces (APIs). The weather forecast providers supply forecasts up to 10 days ahead of key weather parameters such as outdoor temperature, relative humidity, wind speed and wind direction. This approach uses calibrated EnergyPlus models to foresee the errors in the indoor thermal behavior and energy demand caused by the increasing day-ahead weather forecasts. A case study investigated the impact of using up to 7-day weather forecasts on mean indoor temperature and energy demand predictions in a building located in Pamplona, Spain. The main novel concepts in this paper are: first, the characterization of the weather forecast error for a specific weather data provider and location and its effect in the building’s load prediction. The error is calculated based on recorded hourly data so the results are provided on an hourly basis, avoiding the cancel out effect when a wider period of time is analyzed. The second is the classification and analysis of the data hour-by-hour to provide an estimate error for each hour of the day generating a map of hourly errors. This application becomes necessary when the building takes part in the day-ahead programs such as demand response or flexibility strategies, where the predicted hourly load must be provided to the grid in advance. The methodology developed in this paper can be extrapolated to any weather forecast provider, location or building.

Impact Assessment for Building Energy Models Using Observed vs. Third-Party Weather Data Sets

“…MADP, which is also called the MAD/mean in some studies [55], has advantages that overcome some shortcomings of other metrics. It is not infinite when the actual values are zero, is very large when actual values are close to zero, and does not take extreme values when managing low-volume data [55][56][57]. CV(RMSE), which gives a relatively high weight to large variations, is the other percentage metric selected for this study because it is a common metric in energy analysis.…”

Impact of Actual Weather Datasets for Calibrating White-Box Building Energy Models Base on Monitored Data

“…The CV(RMSE) is achieved by weighting the Root Mean Square Error (RMSE) by the mean of the actual data. The measured variability is considered to be error variance by this index and therefore the American Society of Heating, Refrigerating, and Air Conditioning Engineers (ASHRAE) Guideline 14, the Federal Energy Management Program (FEMP) and the International Performance Maintenance and Verification Protocol (IPMVP) recommend its use [30,[35][36][37][38][39][40][41][42]. Secondly, the coefficient of determination R 2 (Equation 2) which is the percentage of variation of the response variable that explains its relationship with one or more predictor variables.…”

Neural Approach in Short-Term Outdoor Temperature Prediction for Application in HVAC Systems

“…Nevertheless, in the literature, many different meteorological parameters (apart from the outdoor temperature) were used for that purpose, such as wind speed, wind direction, dew-point temperature, air pressure, relative air humidity, and solar radiation [43], Temperature characteristics of the building were also considered, like its massive wall, inner and outer surface temperature, and heat flux [44], as well as parameters connected with occupancy: motion, air flowrate, or window opening [45]. On the other hand, the authors [14,46] have also indicated that the outdoor temperature is the most sensitive meteorological parameter with the greatest impact on the energy demand.…”