Energy Consumption Forecasting for University Sector Buildings

Amber, Khuram Pervez; Aslam, Muhammad Waqar; Mahmood, Anzar; Kousar, Anila; Younis, Muhammad Yamin; Akbar, Bilal; Chaudhary, Ghulam Qadar; Hussain, Syed Kashif

doi:10.3390/en10101579

Cited by 85 publications

(39 citation statements)

References 29 publications

(43 reference statements)

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“…Regression approaches, see [2,6], are also widely used in the field of short-term and medium-term load forecasting, including non-linear regression [7] and nonparametric regression [8] methods. Recently, in [9] the authors use linear multiple regression to predict the daily electricity consumption of administrative and academic buildings located at a campus of London South Bank University.…”

Section: Introductionmentioning

confidence: 99%

Load Forecasting for a Campus University Using Ensemble Methods Based on Regression Trees

2018

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Load forecasting models are of great importance in Electricity Markets and a wide range of techniques have been developed according to the objective being pursued. The increase of smart meters in different sectors (residential, commercial, universities, etc.) allows accessing the electricity consumption nearly in real time and provides those customers with large datasets that contain valuable information. In this context, supervised machine learning methods play an essential role. The purpose of the present study is to evaluate the effectiveness of using ensemble methods based on regression trees in short-term load forecasting. To illustrate this task, four methods (bagging, random forest, conditional forest, and boosting) are applied to historical load data of a campus university in Cartagena (Spain). In addition to temperature, calendar variables as well as different types of special days are considered as predictors to improve the predictions. Finally, a real application to the Spanish Electricity Market is developed: 48-h-ahead predictions are used to evaluate the economical savings that the consumer (the campus university) can obtain through the participation as a direct market consumer instead of purchasing the electricity from a retailer.

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Section: Introductionmentioning

confidence: 99%

Load Forecasting for a Campus University Using Ensemble Methods Based on Regression Trees

2018

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“…The correlation coefficient obtained by the models ranged between 91.81% and 99.56%, thereby reflecting the possibilities of using such models for the energy characterization of office buildings; (ii) Qiang et al [47] developed MLR models to estimate the daily mean cooling load in HVAC systems of office buildings in Tianjin (China). The estimations obtained by the models presented a mean absolute percentage error lower than 8% with respect to the real values; (iii) Amber et al [48] developed an MLR to estimate the daily energy consumption in university buildings. The independent variables of the model were external temperature, relative humidity, solar radiation, wind speed, weekday index (1 for working days and 0 for the remaining days), and type of building.…”

Section: Mlrmentioning

confidence: 99%

Estimating Adaptive Setpoint Temperatures Using Weather Stations

et al. 2019

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Reducing both the energy consumption and CO2 emissions of buildings is nowadays one of the main objectives of society. The use of heating and cooling equipment is among the main causes of energy consumption. Therefore, reducing their consumption guarantees such a goal. In this context, the use of adaptive setpoint temperatures allows such energy consumption to be significantly decreased. However, having reliable data from an external temperature probe is not always possible due to various factors. This research studies the estimation of such temperatures without using external temperature probes. For this purpose, a methodology which consists of collecting data from 10 weather stations of Galicia is carried out, and prediction models (multivariable linear regression (MLR) and multilayer perceptron (MLP)) are applied based on two approaches: (1) using both the setpoint temperature and the mean daily external temperature from the previous day; and (2) using the mean daily external temperature from the previous 7 days. Both prediction models provide adequate performances for approach 1, obtaining accurate results between 1 month (MLR) and 5 months (MLP). However, for approach 2, only the MLP obtained accurate results from the 6th month. This research ensures the continuity of using adaptive setpoint temperatures even in case of possible measurement errors or failures of the external temperature probes.

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“…In this sense, the student residence halls, which occupy nearly 24% space and have a share of 18% in the total energy consumption of a typical university campus [8], offer a good opportunity for the CHP technology due to their consistent and year-round energy demands.…”

Section: Introductionmentioning

confidence: 99%

The Significance of a Building’s Energy Consumption Profiles for the Optimum Sizing of a Combined Heat and Power (CHP) System—A Case Study for a Student Residence Hall

et al. 2018

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University buildings, such as student residence halls with year-round consistent energy demands, offer strong opportunities for Combined Heat and Power (CHP) systems. The economic and environmental feasibility of a CHP project is strongly linked with its optimum sizing. This study aims to undertake such an assessment for a CHP system for a student residence hall located in London, the United Kingdom (UK). The study also aims to undertake a sensitivity analysis to investigate the effect of different parameters on the project's economics. Necessary data are collected via interviews with the University's Energy Manager. Modeling of the CHP system is performed using the London South Bank University (LSBU, London, the UK) CHP model. Results demonstrate that optimum sizing of CHP is crucial for achieving higher economic and environmental benefits and strongly depends on the authenticity of the energy consumption data, based on which the CHP is being sized. Use of incorrect energy data could result in an undersized or oversized CHP system, where an oversized system will result in higher negative results compared to an undersized system. Finally, Monto Carlo statistical analysis shows that electricity price is the significant factor that could affect the project's economics. With an increasing spark gap, the payback period decreases, and vice versa.

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Energy Consumption Forecasting for University Sector Buildings

Cited by 85 publications

References 29 publications

Load Forecasting for a Campus University Using Ensemble Methods Based on Regression Trees

Load Forecasting for a Campus University Using Ensemble Methods Based on Regression Trees

Estimating Adaptive Setpoint Temperatures Using Weather Stations

The Significance of a Building’s Energy Consumption Profiles for the Optimum Sizing of a Combined Heat and Power (CHP) System—A Case Study for a Student Residence Hall

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