scholarly journals Probabilistic Load Forecasting for Building Energy Models

Sensors ◽  
2020 ◽  
Vol 20 (22) ◽  
pp. 6525
Author(s):  
Eva Lucas Segarra ◽  
Germán Ramos Ruiz ◽  
Carlos Fernández Bandera
Keyword(s):  
Smart Grids ◽  
Load Forecasting ◽  
Weather Forecast ◽  
Building Energy ◽  
Energy Performance ◽  
Prediction Intervals ◽  
Gaussian Kernel ◽  
Weather Data ◽  
Load Forecast ◽  
Probabilistic Load

In the current energy context of intelligent buildings and smart grids, the use of load forecasting to predict future building energy performance is becoming increasingly relevant. The prediction accuracy is directly influenced by input uncertainties such as the weather forecast, and its impact must be considered. Traditional load forecasting provides a single expected value for the predicted load and cannot properly incorporate the effect of these uncertainties. This research presents a methodology that calculates the probabilistic load forecast while accounting for the inherent uncertainty in forecast weather data. In the recent years, the probabilistic load forecasting approach has increased in importance in the literature but it is mostly focused on black-box models which do not allow performance evaluation of specific components of envelope, HVAC systems, etc. This research fills this gap using a white-box model, a building energy model (BEM) developed in EnergyPlus, to provide the probabilistic load forecast. Through a Gaussian kernel density estimation (KDE), the procedure converts the point load forecast provided by the BEM into a probabilistic load forecast based on historical data, which is provided by the building’s indoor and outdoor monitoring system. An hourly map of the uncertainty of the load forecast due to the weather forecast is generated with different prediction intervals. The map provides an overview of different prediction intervals for each hour, along with the probability that the load forecast error is less than a certain value. This map can then be applied to the forecast load that is provided by the BEM by applying the prediction intervals with their associated probabilities to its outputs. The methodology was implemented and evaluated in a real school building in Denmark. The results show that the percentage of the real values that are covered by the prediction intervals for the testing month is greater than the confidence level (80%), even when a small amount of data are used for the creation of the uncertainty map; therefore, the proposed method is appropriate for predicting the probabilistic expected error in load forecasting due to the use of weather forecast data.

scholarly journals Probabilistic Load Forecasting Optimization for Building Energy Models via Day Characterization

Sensors ◽  
2021 ◽  
Vol 21 (9) ◽  
pp. 3299
Author(s):  
Eva Lucas Segarra ◽  
Germán Ramos Ruiz ◽  
Carlos Fernández Bandera
Keyword(s):  
Optimization Technique ◽  
Load Forecasting ◽  
Weather Forecast ◽  
Building Energy ◽  
Load Forecast ◽  
Energy Saving Potential ◽  
Energy Models ◽  
Building Monitoring ◽  
Probabilistic Load ◽  
Grid Operators

Accurate load forecasting in buildings plays an important role for grid operators, demand response aggregators, building energy managers, owners, customers, etc. Probabilistic load forecasting (PLF) becomes essential to understand and manage the building’s energy-saving potential. This research explains a methodology to optimize the results of a PLF using a daily characterization of the load forecast. The load forecast provided by a calibrated white-box model and a real weather forecast was classified and hierarchically selected to perform a kernel density estimation (KDE) using only similar days from the database characterized quantitatively and qualitatively. A real case study is presented to show the methodology using an office building located in Pamplona, Spain. The building monitoring, both inside—thermal sensors—and outside—weather station—is key when implementing this PLF optimization technique. The results showed that thanks to this daily characterization, it is possible to optimize the accuracy of the probabilistic load forecasting, reaching values close to 100% in some cases. In addition, the methodology explained is scalable and can be used in the initial stages of its implementation, improving the values obtained daily as the database increases with the information of each new day.

scholarly journals Utilising Open Geospatial Data to Refine Weather Variables for Building Energy Performance Evaluation—Incident Solar Radiation and Wind-Driven Infiltration Modelling

Energies ◽  
2021 ◽  
Vol 14 (4) ◽  
pp. 802
Author(s):  
Kristian Skeie ◽  
Arild Gustavsen
Keyword(s):  
Solar Radiation ◽  
Web Service ◽  
Meteorological Data ◽  
Surface Wind ◽  
Building Energy ◽  
Energy Performance ◽  
Geospatial Data ◽  
Weather Data ◽  
Building Site ◽  
Incident Solar Radiation

In building thermal energy characterisation, the relevance of proper modelling of the effects caused by solar radiation, temperature and wind is seen as a critical factor. Open geospatial datasets are growing in diversity, easing access to meteorological data and other relevant information that can be used for building energy modelling. However, the application of geospatial techniques combining multiple open datasets is not yet common in the often scripted workflows of data-driven building thermal performance characterisation. We present a method for processing time-series from climate reanalysis and satellite-derived solar irradiance services, by implementing land-use, and elevation raster maps served in an elevation profile web-service. The article describes a methodology to: (1) adapt gridded weather data to four case-building sites in Europe; (2) calculate the incident solar radiation on the building facades; (3) estimate wind and temperature-dependent infiltration using a single-zone infiltration model and (4) including separating and evaluating the sheltering effect of buildings and trees in the vicinity, based on building footprints. Calculations of solar radiation, surface wind and air infiltration potential are done using validated models published in the scientific literature. We found that using scripting tools to automate geoprocessing tasks is widespread, and implementing such techniques in conjunction with an elevation profile web service made it possible to utilise information from open geospatial data surrounding a building site effectively. We expect that the modelling approach could be further improved, including diffuse-shading methods and evaluating other wind shelter methods for urban settings.

Development of Extreme Reference Years for Building Energy Simulation Scenarios

2019 ◽  
Vol 887 ◽  
pp. 129-139 ◽  
Author(s):  
Giovanni Pernigotto ◽  
Alessandro Prada ◽  
Andrea Gasparella
Keyword(s):  
Development Process ◽  
Building Energy ◽  
Energy Performance ◽  
Solar Irradiation ◽  
Weather Data ◽  
Building Energy Simulation ◽  
Technical Standard ◽  
Alpine Regions ◽  
Cross Correlations

Typical years are developed from the analysis of multi-year series, selecting actual months to assembly in a single reference year, representative of the long-term typical weather. Some statistical techniques are generally involved in the development process to ensure true frequencies, sequences and cross-correlations of the weather quantities: as regard the reference year built according to the European technical standard EN ISO 15927-4:2005, TRYEN, the method is based on the Finkelstein-Schafer statistics. In this work, we exploit the same statistic with a different target: to develop an extreme reference year, ERY, by identifying those candidate months far from being representative of the long-term weather data distribution. These new artificial extreme years are composed of statistically “non-representative” months warmer in the summer and colder in the winter - which means with daily dry bulb temperature and global solar irradiation higher in summer or lower in winter than the long-term averages respectively. The analysis is performed for five Italian localities belonging to the Alpine Regions and to Sicily. Aiming to assess the efficacy of the proposed procedure, TRYEN and ERY are compared and both used to simulate the energy performance of 48 simplified buildings, parametrically built by varying insulation level, windows’ size, orientation and SHGC and kind of opaque elements.

Building energy performance diagnosis using energy bills and weather data

2018 ◽  
Vol 172 ◽  
pp. 181-191 ◽  
Author(s):  
Yang Geng ◽  
Wenjie Ji ◽  
Borong Lin ◽  
Jiajie Hong ◽  
Yingxin Zhu
Keyword(s):  
Building Energy ◽  
Energy Performance ◽  
Weather Data ◽  
Building Energy Performance ◽  
Performance Diagnosis

Bayesian Deep Learning-Based Probabilistic Load Forecasting in Smart Grids

2020 ◽  
Vol 16 (7) ◽  
pp. 4703-4713 ◽  
Author(s):  
Yandong Yang ◽  
Wei Li ◽  
T. Aaron Gulliver ◽  
Shufang Li
Keyword(s):  
Deep Learning ◽  
Smart Grids ◽  
Load Forecasting ◽  
Probabilistic Load

Continuous Monitoring, Modeling, and Evaluation of Actual Building Energy Systems

2014 ◽  
Author(s):  
Maxim L. Sankey ◽  
Sheldon M. Jeter ◽  
Trevor D. Wolf ◽  
Donald P. Alexander ◽  
Gregory M. Spiro ◽  
...  
Keyword(s):  
Energy Consumption ◽  
Real Time ◽  
Continuous Monitoring ◽  
Energy System ◽  
Building Energy ◽  
Energy Performance ◽  
Weather Data ◽  
Practical Procedure ◽  
Heating And Cooling ◽  
Institute Of Technology

Residential and commercial buildings account for more than 40% of U.S. energy consumption, most of which is related to heating, ventilation and air conditioning (HVAC). Consequently, energy conservation is important to building owners and to the economy generally. In this paper we describe a process under development to continuously evaluate a building’s heating and cooling energy performance in near real-time with a procedure we call Continuous Monitoring, Modeling, and Evaluation (CMME). The concept of CMME is to model the expected operation of a building energy system with actual weather and internal load data and then compare modeled energy consumption with actual energy consumption. For this paper we modeled two buildings on the Georgia Institute of Technology campus. After creating our building models, internal lighting loads and equipment plug-loads were collected through electrical sub-metering, while the building occupancy load was recorded using doorway mounted people counters. We also collected on site weather and solar radiation data. All internal loads were input into the models and simulated with the actual weather data. We evaluated the building’s overall performance by comparing the modeled heating and cooling energy consumption with the building’s actual heating and cooling energy consumption. Our results demonstrated generally acceptable energy performance for both buildings; nevertheless, certain specific energy inefficiencies were discovered and corrective actions are being taken. This experience shows that CMME is a practical procedure for improving the performance of actual well performing buildings. With improved techniques, we believe the CMME procedure could be fully automated and notify building owners in real-time of sub-optimal building performance.

Short-term load forecasting coupled with weather profile generation methodology

2017 ◽  
Vol 39 (3) ◽  
pp. 310-327 ◽  
Author(s):  
Guangya Zhu ◽  
Tin-Tai Chow ◽  
Norman Tse
Keyword(s):  
Input Data ◽  
Weather Prediction ◽  
Load Forecasting ◽  
Building Energy ◽  
Weather Data ◽  
Generation Model ◽  
Short Term ◽  
Forecasting Accuracy ◽  
Building Load

Short-term building load forecasting is indispensable in daily operation of future intelligent/green buildings, particularly in formulating system control strategies and assessing the associated environmental impacts. Most previous research works have been focused on studying the advancement in forecasting techniques, but not as much on evaluating the availability of influential factors like the predicted weather profile in the coming hours. This article proposes an improved procedure to predict the building load 24 hours ahead, together with a backup weather profile generating method. The quality of the proposed weather profile generation model and the forecasting procedures were examined through a case study of application to university academic buildings. The results showed that the load forecasting accuracy with the application of either the real weather data on record or of the predicted weather data from the profile generation model is very much similar. This indicates that the weather prediction model is suitable for applying to building load forecasting. Besides, the comparisons between different sets of input data illustrated that the forecasting accuracy can be improved through the input data filtering and regrouping procedures. Practical application: A weather profile prediction technique for use in building energy forecasting was introduced. This can be coupled to a building energy use forecasting model for predicting the hourly consumption profile of the next day. This prediction time span can be crucial for formulating the daily operation plan of the utility systems or for smart micro-grid applications. The appropriateness of the methodology was evaluated through a case study.

Deep ensemble learning based probabilistic load forecasting in smart grids

Energy ◽  
2019 ◽  
Vol 189 ◽  
pp. 116324 ◽  
Author(s):  
Yandong Yang ◽  
Weijun Hong ◽  
Shufang Li
Keyword(s):  
Ensemble Learning ◽  
Smart Grids ◽  
Load Forecasting ◽  
Probabilistic Load
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