Output Temperature Predictions of the Geothermal Heat Pump System Using an Improved Grey Prediction Model

Abstract

This paper presents the Improved Grey Prediction Model, also called IGM (1,1) model, to increase the prediction accuracy of the Grey Prediction Model (GM) model that performs the GHPS output temperature prediction. This was based on correcting the current predicted value by subtracting the error between the previous predicted value and the previous immediate mean of the measured value. Subsequently, the IGM (1,1) model was applied to predict the output temperature of the GHPSs at Oklahoma University, the University Politècnica de València, and Oakland University, respectively. For each GHPS, the model uses a small dataset of 24 data points (i.e., 24 h) for training to predict the output temperature eight hours in advance. The proposed model was verified using three different output temperature datasets; these datasets were also used to validate the power efficiency of the proposed model. In addition, the empirical results show that the proposed IGM (1,1) model significantly improves the simulation (in-sample) and the prediction (out-of-sample) of the output temperature of the GHPS through error reduction, thereby enhancing the GM (1,1) model’s overall accuracy. As a result, the prediction accuracies were compared, and the improved model was found to be more accurate than the GM (1,1) model in both simulation and prediction results for all datasets used.

A mathematical model of catalyst combination design and temperature control in the preparation of C4 olefins through ethanol coupling

“…The other data fluctuate around their means. The grey prediction model GM (1,1) 24,25 could describe the relationship between time and ethanol conversion rate. It was used to predict the ethanol conversion rate (%); the results are shown in Fig.…”

Evaluation of the Coupling Coordination Relationship Between Public Cultural Services and Smart Technology

“…Data are mainly taken from the China Statistical Yearbook and the statistical bulletins of national economic and social development of various provinces. Since there are some missing values in the data of the Tibet Autonomous Region, the paper mainly uses the grayscale prediction model to estimate the missing values [5]. This paper constructs an index system to measure the integration of public cultural services and scientific and smart technology from the above elements, and the weight of the index is calculated by entropy method.…”

“…According to the index system of public cultural services and smart technology, the degree of coupling coordination (degree of integration) of public cultural services and smart technology of 31 provinces (excluding Hong Kong, Macao and Taiwan) in China from 2015 to 2021 is calculated using Formulas (1) to (5), and the results are shown in Fig. 1.…”