Date of Publication


Document Type

Master's Thesis

Degree Name

Master of Science in Mechanical Engineering

Subject Categories

Mechanical Engineering


Gokongwei College of Engineering


Mechanical Engineering

Thesis Adviser

Alvin B. Culaba

Defense Panel Chair

Gerardo L. Augusto

Defense Panel Member

Neil Stephen A. Lopez
Edwin Sybingco


Mixed-use buildings contribute to the sustainable development of cities by providing economic, environmental, and social benefits. However, energy management of these buildings remains a challenge due to their unpredictable energy consumption characteristics and the lack of design guidelines for energy efficiency and sustainability solutions. This study aimed to develop and prototype a prediction model to characterize and forecast the energy consumption of mixed-use buildings and demonstrate its application by developing

an optimization model to determine the design capacities of a proposed integrated renewable- storage energy system. The study applied machine learning techniques in developing and

prototyping the prediction model, specifically k-means algorithm for clustering and support vector machines for forecasting. HOMER Grid software was used in developing the optimization model. The prediction model was initially developed on simulated data from OpenEI database and later prototyped using actual data obtained from The Building Data Genome Project, which was also used for the optimization model. The results of the study show that the prediction model had a performance better than statistical approaches previously developed in the literature and conform to building modeling standards. Improvements in model performance due to the novel integration of the clustering model were also observed in the initial model and specific cases in the prototype. Finally, the optimization results show that the proposed integrated energy system is viable and more economically attractive than stand-alone energy systems and the business-as-usual case for mixed-use buildings.

Abstract Format







Total energy systems (On-site electric power production); Renewable energy sources

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