scholarly journals Robust Building Energy Load Forecasting Using Physically-Based Kernel Models

Energies ◽  
2018 ◽  
Vol 11 (4) ◽  
pp. 862 ◽  
Author(s):  
Anand Prakash ◽  
Susu Xu ◽  
Ram Rajagopal ◽  
Hae Noh
Keyword(s):  
Load Forecasting ◽  
Building Energy ◽  
Kernel Models ◽  
Physically Based

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.

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.

scholarly journals Stacked Boosters Network Architecture for Short-Term Load Forecasting in Buildings

Energies ◽  
2020 ◽  
Vol 13 (9) ◽  
pp. 2370 ◽  
Author(s):  
Tuukka Salmi ◽  
Jussi Kiljander ◽  
Daniel Pakkala
Keyword(s):  
Time Series ◽  
Network Architecture ◽  
State Of The Art ◽  
Multivariate Time Series ◽  
Load Forecasting ◽  
Building Energy ◽  
Short Term ◽  
Univariate Time Series ◽  
Short Term Load Forecasting ◽  
Base Learner

This paper presents a novel deep learning architecture for short-term load forecasting of building energy loads. The architecture is based on a simple base learner and multiple boosting systems that are modelled as a single deep neural network. The architecture transforms the original multivariate time series into multiple cascading univariate time series. Together with sparse interactions, parameter sharing and equivariant representations, this approach makes it possible to combat against overfitting while still achieving good presentation power with a deep network architecture. The architecture is evaluated in several short-term load forecasting tasks with energy data from an office building in Finland. The proposed architecture outperforms state-of-the-art load forecasting model in all the tasks.

Combining a Detailed Building Energy Model with a Physically-Based Urban Canopy Model

2011 ◽  
Vol 140 (3) ◽  
pp. 471-489 ◽  
Author(s):  
Bruno Bueno ◽  
Leslie Norford ◽  
Grégoire Pigeon ◽  
Rex Britter
Keyword(s):  
Urban Canopy ◽  
Building Energy ◽  
Energy Model ◽  
Urban Canopy Model ◽  
Physically Based ◽  
Canopy Model
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