Baseline energy modelling in an educational building campus for measurement and verification

Mustapa, Rijalul Fahmi; Dahlan, NY; Yassin, Ihsan Mohd; Nordin, Atiqah Hamizah Mohd; Mahadan, Mohd Ezwan

doi:10.1109/iceese.2017.8298383

Cited by 10 publications

(4 citation statements)

References 8 publications

Supporting

Mentioning

Contrasting

Order By: Relevance

“…As such, more education buildings will be developed, thus increase the energy use of commercial sector from its current share. In campus buildings, the dynamic consumption pattern and lack of usage control in classrooms and laboratory often result in significant waste of energy [6]. In order to reduce electricity consumption and promote consumer's demand response, Tenaga Nasional Berhad (TNB) has established a time-based electricity tariff called enhanced time-of-use tariff (ETOU) for commercial and industrial consumers [7].…”

Section: Introductionmentioning

confidence: 99%

Optimum energy management strategy with enhanced time of use tariff for campus building using particle swarm optimization

Mahmud

Dahlan

2022

IJEECS

View full text Add to dashboard Cite

Enhanced time of use (ETOU) tariff was introduced by Tenaga Nasional Berhad (TNB) in 2016 to promote demand response for commercial and industrial consumers. The ETOU tariff scheme offers different tariff rates at different times of the day. However, increment of electricity expenses might occur if consumers fail to optimally shift their consumption to lower rate hours and causing higher usage during peak hours. Furthermore, the time-based pricing tariff is still new to Malaysian consumers, thus consumers have lack of knowledge to perform demand response. Therefore, this research proposes an optimum load management strategy under the ETOU tariff for a commercial building using particle swarm optimization (PSO). The model was applied for Universiti Teknologi MARA (UiTM) Complex Engineering Shah Alam. Load profile of five different buildings in the complex were used as inputs for the study. The analyses were carried out at different controlled loads weightage factors ranged from 10-40% for load shifting strategy to determine the optimal solution. Results show the electricity cost decreases in all the controlled load weightage factors tested on the buildings after applying the load management strategy. The weightage factor of 40% provides the best solution for all buildings, saving 1-4% on the monthly bills.

show abstract

Section: Introductionmentioning

confidence: 99%

Optimum energy management strategy with enhanced time of use tariff for campus building using particle swarm optimization

Mahmud

Dahlan

2022

IJEECS

View full text Add to dashboard Cite

show abstract

“…Recent study by [7][8] involved a groups of building in United States to study the impact of building retrofitting projects to energy consumption as well as energy saving. A number of authors from Malaysia have applied M&V to study the impact of energy saving in the commercial building as well as educational building [9][10]. Most of the widely used M&V application were energy efficiency lighting retrofit projects to improve the efficiency of the lighting system and to reduce the energy consumption [11][12][13][14].…”

Section: Introductionmentioning

confidence: 99%

Development of option c measurement and verification model using hybrid artificial neural network-cross validation technique to quantify saving

Adnan

Dahlan

Musirin

2020

IJ-AI

View full text Add to dashboard Cite

This paper aims to develop a hybrid artificial neural network for Option C Measurement and Verification model to predict monthly building energy consumption. In this work, baseline energy model development using artificial neural networks embedded with artificial bee colony optimization and cross validation technique for a small dataset were considered. Artificial bee colony optimization with coefficient of correlation fitness function was used in optimizing the neural network training process and selecting the optimal values of initial weights and biases. Working days, class days and cooling degree days were used as input meanwhile monthly electricity consumption as an output of artificial neural network. The results indicated that this hybrid artificial neural network model provided better prediction results compared to the other model. The best model with the highest value of coefficient of correlation was selected as the baseline model hence is used to determine the saving.

show abstract

“…The selection of variables is extremely important in the creation of baseline energy models. Mustapa et al (2017) used the simple regression method and multiple variable regression method with different variables to develop models for educational buildings to identify the variables with the greatest effect on energy consumption in educational buildings, and compared the mean squared error (MSE), root mean squared error (RMSE), and mean absolute percentage error (MAPE) of the constructed models. Regardless of whether the energy consumption model is used in the estimation of the minimum energy consumption in energy-saving control or as an energy baseline for the comparison of energy performance, the prediction accuracy of the model is extremely critical.…”

Section: Introductionmentioning

confidence: 99%

Applying the naïve Bayes classifier to HVAC energy prediction using hourly data

Lin

Liu

et al. 2019

Microsyst Technol

View full text Add to dashboard Cite

Heating ventilation and air conditioning (HVAC) accounts for approximately 50% of the total energy consumption of buildings. Therefore, many studies have been focused on the simulation and optimal control of HVAC power consumption or the prediction of energy consumption through the construction of energy consumption models and the improvement of HVAC power consumption through energy management methods. The prediction of energy consumption by optimal energy-saving control or energy baseline is dependent on an accurate energy consumption model, however, the accuracy of the energy consumption model is influenced by the model variables. In addition, different operating periods and load conditions also lead to different changes in energy consumption, which will affect the accuracy of optimal energy consumption control or prediction of energy consumption. The present study proposes a method to enhance the accuracy and sensitivity of HVAC power consumption prediction, which involves the use of a clustering technique to locate clusters with similar information within hourly data, the construction of energy consumption models by converting the clustered hourly data into monthly data, and the application of the proposed Naïve Bayes classifier to classify hourly data under different operating conditions into the energy consumption model with the smallest prediction error. A multiple variable regression model and an artificial neural network (ANN) model were compared with the models developed in the present study, and the normalized mean bias error (NMBE) and the coefficient of variation of the root mean squared error (Cv-RMSE) were used as criteria for the predicted energy consumption values.

show abstract

Baseline energy modelling in an educational building campus for measurement and verification

Cited by 10 publications

References 8 publications

Optimum energy management strategy with enhanced time of use tariff for campus building using particle swarm optimization

Optimum energy management strategy with enhanced time of use tariff for campus building using particle swarm optimization

Development of option c measurement and verification model using hybrid artificial neural network-cross validation technique to quantify saving

Applying the naïve Bayes classifier to HVAC energy prediction using hourly data

Contact Info

Product

Resources

About