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.

Highlights

  • The European Energy Efficiency Directive establishes a set of binding measures to help the EU reach its 20% energy efficiency target by 2020 [1]

  • In this study, the solar radiation was not included as a forecast parameter and the data from Navarra Government weather station was used in the weather files

  • For the analysis of the results, the error indexes showed in Section 4 are calculated using the hourly simulation outputs and comparing the results generated with the real weather data (WR) against the results generated with each forecast weather file (1DA to 7DA and CMB) for the three parameters: outdoor temperature, the mean indoor temperature and the energy demand

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Summary

Introduction

The European Energy Efficiency Directive establishes a set of binding measures to help the EU reach its 20% energy efficiency target by 2020 [1]. The use of weather forecast allows the building to adapt better to the climate of the near future leading the building to be more resilient to increasing extreme weather events, which has become essential to reduce the climate change effects [3,4] One such energy management optimization strategy is model predictive control (MPC), around which current research efforts are evolving. Demonstrated a novel approach that develops a Representational State Transfer (REST) API for users to obtain site-specific historical and near-future weather forecast data in Energy Plus Weather File (EPW) format for building simulation using the free online toolchain Many of these providers offer forecasts more than 1 day-ahead (up to a 10-day weather forecast, i.e., provide forecast weather data predicted 10 days before), its accuracy decreases as time moves further forward [28].

Test Site Description
Weather Files Generation
Sensitivity Analysis of Weather Parameters
BEM Simulation and Error Metrics Calculation
Analysis of the Results
Application of the Methodology
Findings
Conclusions
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