TaghizadehVaghefi, Seyedabolfazl. Energy use forecast and model predictive control of building complexes. Retrieved from https://doi.org/doi:10.7282/T3HT2QZ1
DescriptionU.S. households and commercial buildings consume approximately 40 percent of total energy conversion in the U.S. and account for 72 percent of total U.S. electricity consumption. Commercial building energy demand, in particular, doubled between 1980 and 2000 and has increased 50 percent since then. Developing innovative technologies and building energy-efficiency methods are therefore essential for U.S. national interests and a sustainable energy future. In this thesis, an optimal framework for forecasting and optimization of energy consumption for building complexes is developed. For forecasting purposes, a hybrid time series-regression model is introduced to combine regression models and seasonal autoregressive moving average models to accurately forecast energy usage at both the building and the community/campus level. For optimization purposes, this thesis proposes an optimal control strategy at the building level, which consists of two main phases. In the first phase, a set of offline data – either generated by a whole building simulation platform or measured from a real building – is used to develop models that capture the dynamic behavior of building energy usage. In the second phase, the models are fed into an optimization model that computes the optimal control variables of the building. The optimization model is a Multi-objective Dynamic Programing model that minimizes total operating energy cost and demand charges as well as total deviation from thermal comfort bounds. In addition, the proposed control strategy is adaptive, so that it updates both the estimation and the optimization steps as soon as it receives new measured data. A data-driven risk-based framework is also proposed to predict and control industrial loads in non-residential buildings. In this framework, a set of predictive analysis tools is employed to allocate industrial load profiles into a particular set of classes. Load profiles within the same class have lower variance and follow the same pattern. Then, a generalized linear model (GLM) is used to predict the probability of having stochastic industrial loads coming online over rolling time windows. Finally, for controlling demand response to avoid demand charges, the proposed framework provides the necessary tools to institute load shedding or load shifting strategies.