Machine learning-based modeling of heating process, case study: a greenhouse prototype

Abstract

ABSTRACT Heating demand is the most energy-consuming aspect of controlled environment agriculture (CEA) and seeks the attention of researchers and the commercial side of the industry. Here an experimental methodology to model the microclimate by considering the capacity of the heater as a variable is introduced. Different tests were applied on an exclusive prototype with broad capabilities to prepare sets of data for developing the noted model. A four-layered fully connected neural network which is considered one of the most used machine learning approaches was employed due to its perception for this application. Five different heater capacities with the same initial conditions warmed the microclimate till reaching the steady-state condition. These data were used to train the algorithm capable of predicting the whole process of heating. Two other tests, one with a capacity inside the range and one outside, were conducted additionally in order to validate the model. This model can be used in various optimization problems with great width. It can be used to run different types of simulations to solve an optimization problem in which minimum energy is desired, or to find the optimum heating capacity which satisfies the needs of a specific greenhouse.