Artificial Neural Network Optimization in Prediction Baseline Energy Consumption to Quantify Energy Savings in Commercial Building
| Autor: | Nofri Yenita Dahlan, Ismail Musirin, Wan Nazirah Wan Md Adnan |
|---|---|
| Rok vydání: | 2020 |
| Předmět: |
Mathematical optimization
Artificial neural network Mean squared error Computer science 020209 energy Computer Science::Neural and Evolutionary Computation 0211 other engineering and technologies Particle swarm optimization 02 engineering and technology Energy consumption Mean absolute percentage error Chiller boiler system 021105 building & construction 0202 electrical engineering electronic engineering information engineering Evolutionary programming Energy (signal processing) |
| Zdroj: | 2020 11th IEEE Control and System Graduate Research Colloquium (ICSGRC). |
| DOI: | 10.1109/icsgrc49013.2020.9232655 |
| Popis: | This paper presents a proper baseline energy model of a chiller system for the measurement and verification activity was developed. In measurement and verification, the baseline energy has been modelled using linear regression in finding the correlation between input and output variables. Baseline energy model was proposed applying the Artificial Neural Network. Three optimization methods, Evolutionary Programming, Particle Swarm Optimization and Artificial Bee Colony are hybridized with Artificial Neural Network. These methods were used to optimize the training process and selecting the optimal values of Artificial Neural Network initial weights and biases. The coefficient of correlation, Mean Square Error, Mean Absolute Percentage Error and Standard Error were used to measure the model's accuracy. The dataset composed of three input variables that were affecting the energy consumption of a chiller system were selected namely operating time, refrigerant tonnage and differential temperature. Meanwhile, the output was energy consumption of the building's chiller system. These three Hybrid Artificial Neural Network techniques were then compared with Linear Regression and Artificial Neural Network. The results revealed that Artificial Bee Colony Hybrid with Artificial Neural Network outperforms other methods. This selected method was further used to quantify the chiller system retrofitting energy saving. The energy saving obtained from this model was 165,478.46 kWh. |
| Databáze: | OpenAIRE |
| Externí odkaz: |
|