Predicting Peak Energy Demand for an Office Building Using Artificial Intelligence (AI) Approaches

2021 ◽  
Author(s):  
Yuxuan Chen ◽  
Patrick Phelan
Keyword(s):  
Artificial Intelligence ◽  
Energy Demand ◽  
Prediction Models ◽  
Meteorological Data ◽  
Building Energy ◽  
Energy Performance ◽  
Training Data ◽  
Office Building ◽  
Support Vector ◽  
Peak Energy

Abstract Due to the technological advancement in smart buildings and the smart grid, there is increasing desire of managing energy demand in buildings to achieve energy efficiency. In this context, building energy prediction has become an essential approach for measuring building energy performance, assessing energy system efficiency, and developing energy management strategies. In this study, two artificial intelligence techniques (i.e., ANN = artificial neural networks and SVR = support vector regression) are examined and used to predict the peak energy demand to estimate the energy usage for an office building on a university campus based on meteorological and historical energy data. Two-year energy and meteorological data are used, with one year for training and the following year for testing. To investigate the seasonal load trend and the prediction capabilities of the two approaches, two experiments are conducted relying on different scales of training data. In total, 10 prediction models are built, with 8 models implemented on seasonal training datasets and 2 models employed using year-round training data. It is observed that a backpropagation neural network (BPNN) performs better than SVR when dealing with more data, leading to stable generalization and low prediction error. When dealing with less data, it is found that there is no dominance of one approach over another.

scholarly journals Towards an Automated, Fast and Interpretable Estimation Model of Heating Energy Demand: A Data-Driven Approach Exploiting Building Energy Certificates

Energies ◽  
2019 ◽  
Vol 12 (7) ◽  
pp. 1273 ◽  
Author(s):  
Antonio Attanasio ◽  
Marco Piscitelli ◽  
Silvia Chiusano ◽  
Alfonso Capozzoli ◽  
Tania Cerquitelli
Keyword(s):  
Decision Tree ◽  
Energy Demand ◽  
Building Energy ◽  
Energy Performance ◽  
Primary Energy ◽  
Machine Learning Algorithms ◽  
Data Driven ◽  
Support Vector ◽  
Estimation Model ◽  
Primary Energy Demand

Energy performance certification is an important tool for the assessment and improvement of energy efficiency in buildings. In this context, estimating building energy demand also in a quick and reliable way, for different combinations of building features, is a key issue for architects and engineers who wish, for example, to benchmark the performance of a stock of buildings or optimise a refurbishment strategy. This paper proposes a methodology for (i) the automatic estimation of the building Primary Energy Demand for space heating ( P E D h ) and (ii) the characterization of the relationship between the P E D h value and the main building features reported by Energy Performance Certificates (EPCs). The proposed methodology relies on a two-layer approach and was developed on a database of almost 90,000 EPCs of flats in the Piedmont region of Italy. First, the classification layer estimates the segment of energy demand for a flat. Then, the regression layer estimates the P E D h value for the same flat. A different regression model is built for each segment of energy demand. Four different machine learning algorithms (Decision Tree, Support Vector Machine, Random Forest, Artificial Neural Network) are used and compared in both layers. Compared to the current state-of-the-art, this paper brings a contribution in the use of data mining techniques for the asset rating of building performance, introducing a novel approach based on the use of independent data-driven models. Such configuration makes the methodology flexible and adaptable to different EPCs datasets. Experimental results demonstrate that the proposed methodology can estimate the energy demand with reasonable errors, using a small set of building features. Moreover, the use of Decision Tree algorithm enables a concise interpretation of the quantitative rules used for the estimation of the energy demand. The methodology can be useful during both designing and refurbishment of buildings, to quickly estimate the expected building energy demand and set credible targets for improving performance.

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.

Prediction Models for Truck Accidents at Freeway Ramps in Washington State Using Regression and Artificial Intelligence Techniques

1998 ◽  
Vol 1635 (1) ◽  
pp. 30-36 ◽  
Author(s):  
Wael H. Awad ◽  
Bruce N. Janson
Keyword(s):  
Artificial Intelligence ◽  
Neural Networks ◽  
Linear Regression ◽  
Prediction Models ◽  
Washington State ◽  
Training Data ◽  
Coefficient Of Determination ◽  
Training Process ◽  
Truck Accidents ◽  
High Level

Three different modeling approaches were applied to explain truck accidents at interchanges in Washington State during a 27-month period. Three models were developed for each ramp type including linear regression, neural networks, and a hybrid system using fuzzy logic and neural networks. The study showed that linear regression was able to predict accident frequencies that fell within one standard deviation from the overall mean of the dependent variable. However, the coefficient of determination was very low in all cases. The other two artificial intelligence (AI) approaches showed a high level of performance in identifying different patterns of accidents in the training data and presented a better fit when compared to the regression model. However, the ability of these AI models to predict test data that were not included in the training process showed unsatisfactory results.

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

2020 ◽  
Vol 12 (17) ◽  
pp. 6788 ◽  
Author(s):  
Eva Lucas Segarra ◽  
Germán Ramos Ruiz ◽  
Vicente Gutiérrez González ◽  
Antonis Peppas ◽  
Carlos Fernández Bandera
Keyword(s):  
Energy Demand ◽  
Meteorological Data ◽  
Building Energy ◽  
Third Party ◽  
Weather Data ◽  
Data Sets ◽  
Weather Station ◽  
Energy Models ◽  
The Impact ◽  
Energy Strategies

The use of building energy models (BEMs) is becoming increasingly widespread for assessing the suitability of energy strategies in building environments. The accuracy of the results depends not only on the fit of the energy model used, but also on the required external files, and the weather file is one of the most important. One of the sources for obtaining meteorological data for a certain period of time is through an on-site weather station; however, this is not always available due to the high costs and maintenance. This paper shows a methodology to analyze the impact on the simulation results when using an on-site weather station and the weather data calculated by a third-party provider with the purpose of studying if the data provided by the third-party can be used instead of the measured weather data. The methodology consists of three comparison analyses: weather data, energy demand, and indoor temperature. It is applied to four actual test sites located in three different locations. The energy study is analyzed at six different temporal resolutions in order to quantify how the variation in the energy demand increases as the time resolution decreases. The results showed differences up to 38% between annual and hourly time resolutions. Thanks to a sensitivity analysis, the influence of each weather parameter on the energy demand is studied, and which sensors are worth installing in an on-site weather station are determined. In these test sites, the wind speed and outdoor temperature were the most influential weather parameters.

scholarly journals Flattening the Electricity Demand Profile of Office Buildings for Future-Proof Smart Grids

Energies ◽  
2020 ◽  
Vol 13 (9) ◽  
pp. 2357
Author(s):  
Rick Cox ◽  
Shalika Walker ◽  
Joep van der Velden ◽  
Phuong Nguyen ◽  
Wim Zeiler
Keyword(s):  
Smart Grids ◽  
Energy Demand ◽  
Prediction Models ◽  
Storage System ◽  
Demand Forecasting ◽  
Building Energy ◽  
Electricity Demand ◽  
Electricity Load ◽  
Load Shape ◽  
Demand Profile

The built environment has the potential to contribute to maintaining a reliable grid at the demand side by offering flexibility services to a future Smart Grid. In this study, an office building is used to demonstrate forecast-driven building energy flexibility by operating a Battery Electric Storage System (BESS). The objective of this study is, therefore, to stabilize/flatten a building energy demand profile with the operation of a BESS. First, electricity demand forecasting models are developed and assessed for each individual load group of the building based on their characteristics. For each load group, the prediction models show Coefficient of Variation of the Root Mean Square Error (CVRMSE) values below 30%, which indicates that the prediction models are suitable for use in engineering applications. An operational strategy is developed aiming at meeting the flattened electricity load shape objective. Both the simulation and experimental results show that the flattened load shape objective can be met more than 95% of the time for the evaluation period without compromising the thermal comfort of users. Accurate energy demand forecasting is shown to be pivotal for meeting load shape objectives.

Sign in / Sign up

Export Citation Format

Share Document