Estimation of commercial building energy consumption with machine learning

The estimation of energy consumption in commercial buildings holds immense significance in the pursuit of sustainable energy management and the efficient allocation of resources. As energy demands continue to rise, optimizing energy consumption becomes crucial for reducing carbon footprints and enhancing cost-effectiveness. In response to these challenges, this research endeavors to leverage the power of machine learning (ML) techniques to accurately predict energy consumption in commercial buildings. By employing ML algorithms, this study seeks to improve energy efficiency, lower operational costs, and facilitate informed decision-making in building energy management. To achieve these objectives, the research employs eight ML algorithms, namely Support Vector Machines (SVM), Random Forest (RF), Extra Trees, Linear Regression, Lasso, Gradient Boosting Regressor (GBR), Multilayer Perceptron (MLP), and a novel stacking model. Each of these algorithms is well-known for its predictive capabilities and ability to handle various types of data. The research methodology encompasses the development and assessment of predictive models using a robust and extensive dataset, carefully collected from commercial buildings. Before applying the ML algorithms, the dataset undergoes rigorous preprocessing to ensure data quality and enhance model performance. Normalization, data cleaning, and feature selection techniques, such as ANOVA and Relief F, are employed to eliminate noise and irrelevant information, making the dataset suitable for training and evaluation. The research splits the data into training and testing sets in a balanced ratio of 70:30, ensuring that the models are trained on a substantial portion of the data while being evaluated on unseen samples. ii This balanced data splitting allows for unbiased evaluation and comparison of the performance of the ML algorithms. Remarkably, the results reveal that the stacking model, a novel approach that synergizes multiple models' strengths, outperforms all other ML algorithms and state-of-the-art approaches. The stacking model showcases an impressive Mean Absolute Error (MAE) of 0.01286, Mean Squared Error (MSE) of 0.00054, and Root Mean Squared Error (RMSE) of 0.02328. These exceptional performance metrics demonstrate the stacking model's potential as a highly effective tool for accurately estimating energy consumption in commercial buildings. The research findings are further validated by comparing the results with related works in the field. The stacking model consistently outperforms existing approaches, reaffirming its superiority in accurately predicting energy consumption. This model's success can be attributed to its ability to combine diverse models, leveraging their complementary strengths to capture complex relationships within the energy consumption data. The adoption of the stacking model as the best-performing ML algorithm in energy consumption estimation carries significant implications for sustainable energy practices in the built environment. Accurate energy consumption prediction empowers building managers to make informed decisions on energy allocation, implement energy-saving measures, and optimize energy usage. By enabling data-driven energy management, the stacking model supports the advancement of sustainable energy practices, contributing to the reduction of carbon emissions and the promotion of environmentally friendly building operations. In conclusion, this research showcases the immense potential of machine learning techniques in accurately estimating energy consumption in commercial buildings. The stacking model's exceptional performance, when compared with other ML algorithms and state-of-the-art approaches, highlights its role as a transformative tool in achieving energy efficiency and sustainable energy management. As the world continues to prioritize sustainability, the implementation of the stacking model can significantly contribute to building a greener and more energy-efficient future.