Prediction Model for the Internal Temperature of a Greenhouse with a Water-to-Water Heat Pump Using a Pellet Boiler as a Heat Source Using Building Energy Simulation

Abstract

Although smart farms are considered an alternative to traditional agriculture, they require large amounts of energy and high investment costs, hindering their efficient implementation. In the Republic of Korea, the energy supply is primarily for heating rather than cooling, necessitating the accurate prediction of the greenhouse internal temperature to determine the feasibility of agricultural management while using renewable energy. This study developed a model (TRNSYS) for predicting the internal temperature of a greenhouse using building energy simulation. A greenhouse heating experiment was conducted using a hybrid heating system simulated by TRNSYS to analyze the prediction model. The regression analysis of the experimental and simulation results revealed an R2 and RMSE of 0.8834 and 3.61, respectively. A comparative analysis was conducted with the existing hot air heating system to evaluate the heating performance and economic feasibility of the hybrid system. Overall, the heating performance exhibited satisfactory results, whereas the economic analysis, based on life cycle cost, revealed a cost reduction effect of 9.45%. Hence, greenhouse heating using renewable energy can replace conventional fossil fuels with economic advantages. Moreover, the prediction of the internal temperature of the greenhouse will facilitate the design of a systematic smart farm business to prevent duplicate investment.

Calibration and Implementation of a Dynamic Energy Balance Model to Estimate the Temperature in a Plastic-Covered Colombian Greenhouse

“…In addition, the use of modeling techniques has been another tool used to determine temperature and other microclimatic variables. These models have been proposed by numerous researchers who have worked on this subject over the last seven decades [10][11][12]. The typology of implemented models can be divided into physical models based on mass and energy balance equations and black box models using learning algorithms [13].…”

A Comprehensive Technoeconomic and Environmental Evaluation of a Hybrid Renewable Energy System for a Smart Farm in South Korea

“…In addition, the excess energy is supplied to the grid or stored. However, the smart farms require high investment costs to meet the high energy demands which are mainly required for the heating purposes [6]. About 53.4% of the total energy consumed by the agricultural sector is devoted to the heating and cooling systems, where this demand is mainly covered by fossil fuels [7].…”

“…Therefore, the need to develop an efficient and cost-effective energy system based on renewable energy sources is necessary to meet the carbon neutralization targets [8,9]. It is important to note that the agricultural sector in the Republic of Korea targets to achieve 4.8% of the total national targeted emission reduction of CO 2 [6]. This can be achieved by adopting different renewable energy sources in hybrid configurations.…”