Availability Performances of Ground-Coupled Heat Pump Systems

The paper presents aspects regarding the reliability and availability of ground-coupled heat pump systems (GCHP). Basic concepts of predictive reliability are introduced and the two modes of reliability analyses (quantitative and qualitative) are shown specifically for PCSS. In order to be able to model the availability of GCHP systems, global reliability indicators must be determined. Furthermore, failure manners and their effects on system reliability, as well as the graphical-analytical methods that could be applied to GCHP systems are analyzed. Finally, a case study for the experimental system installed in Thermodynamics Laboratory belonging to Energy Engineering Faculty is shown

Energy Performance Analysis of the first Research-only Ground Coupled Heat Pump in Romania

One laboratory belonging to Energy Engineering Faculty is heated with a ground-coupled heat pump (GCHP) system having two types of ground heat exchangers: a vertical one and a horizontal one. This paper presents, at first, several coefficients of performance that can be defined for heat pumps together with their usability. Then, an energy efficiency model is set specially for CGHP systems. In this paper the simulation of energy performances is done only for the vertical heat exchanger (borehole heat exchanger) having two configurations: single-U and double-U. All simulation results range between normal values and can be considered realistic

Energy Performance Analysis of a Heat Supply System of a University Campus

The energy efficiency of a system and the performance level of its equipment and installations are the two key elements based on which the investment decision in its modernization is made. They are also very important for setting up optimal operation strategies. The energy audit is a well-known and worldwide recognized tool for calculating energy performance indicators and developing improvement measures. This paper is a synthesis of the energy audit results performed for a district heating network that uses geothermal energy as its primary source of energy. The location of the heating system is inside a university campus. The first part explains the necessity of a comprehensive study on district heating networks and introduces the defining elements that characterize the analyzed equipment and installations. The complex energy balance methodology that has been developed and applied to this district heating system is presented in the second part of the paper. Next, the methodology for collecting the input data for the energy and mass balance is explained. In the final part, the numerical values of the performance indicators and the technical measures that must be applied to improve energy efficiency are shown, and conclusions are drawn

Residential Short-Term Load Forecasting during Atypical Consumption Behavior

Short-term load forecasting (STLF) is a fundamental tool for power networks’ proper functionality. As large consumers need to provide their own STLF, the residential consumers are the ones that need to be monitored and forecasted by the power network. There is a huge bibliography on all types of residential load forecast in which researchers have struggled to reach smaller forecasting errors. Regarding atypical consumption, we could see few titles before the coronavirus pandemic (COVID-19) restrictions, and afterwards all titles referred to the case of COVID-19. The purpose of this study was to identify, among the most used STLF methods—linear regression (LR), autoregressive integrated moving average (ARIMA) and artificial neural network (ANN)—the one that had the best response in atypical consumption behavior and to state the best action to be taken during atypical consumption behavior on the residential side. The original contribution of this paper regards the forecasting of loads that do not have reference historic data. As the most recent available scenario, we evaluated our forecast with respect to the database of consumption behavior altered by different COVID-19 pandemic restrictions and the cause and effect of the factors influencing residential consumption, both in urban and rural areas. To estimate and validate the results of the forecasts, multiyear hourly residential consumption databases were used. The main findings were related to the huge forecasting errors that were generated, three times higher, if the forecasting algorithm was not set up for atypical consumption. Among the forecasting algorithms deployed, the best results were generated by ANN, followed by ARIMA and LR. We concluded that the forecasting methods deployed retained their hierarchy and accuracy in forecasting error during atypical consumer behavior, similar to forecasting in normal conditions, if a trigger/alarm mechanism was in place and there was sufficient time to adapt/deploy the forecasting algorithm. All results are meant to be used as best practices during power load uncertainty and atypical consumption behavior