Alcances y limitaciones de las compensaciones ambientales basadas en la

Model Predictive Control (MPC) is a form of direct digital control with capa- bilities to handle systems with multiple inputs and multiple constraints. Fur- thermore, it has the advantage of being able to assess an entire schedule of future actions rather than just a single action at the current time. This en- sures that the controller does not just make ‘greedy’ decisions for maximum short-term gain but considers a longer-term, more predictive approach. MPC controllers normally sit a layer above more traditional control mechanisms such as proportional, integral and derivative (PID) controllers to overcome their lack of foresight and stability issues [326]. Recent reviews concluded that MPC is both a popular and effective approach in the literature to manage energy use within buildings [324, 327]. The advantages of MPC include it’s ability to factor in variation in external factors such as occupancy, weather and pricing signals, it can exploit a building’s thermal mass, and it can shift load from energy peaks. Broadly the MPC procedure works as follows. The control problem will have both a prediction horizon T made up of N discrete control steps of size t. The

control procedure will aim to optimise one or several input signals or schedules over the entire control horizon T . This is achieved through an internal model which can accurately predict the output signal as a function of the inputs sig- nals (amongst other variables). This model is integrated with an optimisation procedure which can find the optimal or near-optimal input signal to minimise or maximise the selected objective. However, it will only implement the in- put signal over the first control step t before re-optimising and going through the entire MPC procedure again. This ‘sliding window’ approach ensures that the control procedure has the foresight to make intelligent long-term decisions whilst also remaining agile enough to react to changes in circumstances such as disturbances or forecast errors.

For greater clarity on the MPC procedure a generic diagram showing three consecutive timesteps is given in Figure 3.6. Note how the future input signal from t0 to t1 is implemented in the following timestep but the remainder of the

future input signal is free to change to react to updated forecasts or errors in previous predictions. This also has a direct consequence on the future, predicted output signal. A final point to notice is the distance between t0 and

T is identical in the case of all three timesteps, it has simply been translated to the right by a time of t, as this time has been allowed to pass after the initial control procedure was started.

3.5

Conclusion

This Chapter has aimed to outline the core methodology used to conduct this research project. The Chapter began by providing a brief introduction to the available research methodologies and justification for the methodology chosen in this case. It detailed the research approach which was made up of three stages; a thorough literature review, iterative learning through participation in research projects, and finally application of the gained knowledge to extend the state-of-the-art. To tackle the research question provided in Chapter 1, a se- ries of case studies were developed. This Chapter describes the case studies and illustrates how they were formed through interaction with real pilot sites. Finally, a brief introduction to three core techniques was provided. Namely, these are artificial neural networks, genetic algorithms, and model predictive control. These techniques re-occur throughout the thesis so are best explained at this stage.

Figure 3.6: Model predictive control procedure - a) Timestep 1, b) Timestep 2, c) Timestep 3

Management

As demonstrated in the literature review provided in Chapter 2, the optimisa- tion of building energy demand is an active research field with several pro- posed methodologies. This chapter will outline the modelling and optimisation methodology behind a novel zone-level building heating controller. The per- formance of this solution will be compared to a static, baseline scenario to demonstrate the effectiveness of a more context aware, predictive controller.

4.1

Revisiting the Research Question

Specifically, this chapter aims to address research question 2, restated here as:

Can predictive control of building energy demand with consideration of ex- ternal factors lead to reductions in energy cost and improve demand-side flex- ibility?

To provide validation of the proposed method, the optimisation will be applied to a case study building and will be compared to a baseline scenario which uses traditional thermal controls. Additional comparisons will also be made against a controller that uses a similar methodology but applied at a building- level rather than a zone-level. All controllers will be operated as day ahead controllers, meaning they optimise once at the beginning of the day, and as MPC where they optimise every hour. This range of operating modes will allow a wider evaluation of the importance of zone-level control vs building-level and MPC vs Non-MPC. To assess the flexibility of building demand control, the optimisation methodology will be able to minimise energy consumption or the cost of energy subject to a ToU energy tariff. The ability to shift energy demand subject to external energy costs will be increasingly important in the following chapters.

The optimisation methodology described in this Chapter was originally pub- lished in Reynolds et al. [328] and reformatted and expanded for this thesis.

This work built upon the initial, proof of concept investigations conducted in Reynolds et al. [114].