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This paper focused on the performance monitoring and modeling of a 6.0 kW, 2000 L hybrid direct expansion solar assisted heat pump (DX-SAHP) water heater used for the production of hot water in a university students’ accommodation with 123 females. The data of total electrical energy consumed, volume of hot water consumed, ambient temperature, relative humidity, and solar irradiance were obtained from the data acquisition systems and analyzed in conjunction with the energy factor (EF) of the system. A multiple linear regression model was developed to predict the EF. The EF of the hybrid DX-SAHP water heater was determined from the summation of the coefficient of performance (COP) of the heat pump unit and the solar fraction (SF) of the solar collectors. The operations of the hybrid energy system were analyzed based on three phases (first phase from 00:00–08:00, second phase from 08:30–18:30, and third phase from 19:00–23:30) over 24 h for the entire monitoring period. The average EF of the hybrid energy system per day during the second phase of operation was 4.38, while the SF and COP were 0.697 and 3.683, respectively. The developed multiple linear regression model for the hybrid DX-SAHP water heater accurately predicted the determined EF.

Biogas technology is rapidly gaining market penetration, and the type of digesters employed in the harnessing of the biogas from biodegradable waste is crucial in enhancing the total viable bacteria counts. This study focused on the exploration of input parameter (number of days, daily slurry temperature, and pH) and target (total viable bacteria counts) datasets from anaerobic balloon digester charged with cow manure using data acquisition system and standard methods. The predictors were ranked according to their weights of importance to the desired targets using the reliefF test. The complete dataset was randomly partitioned into testing and validated samples at a ratio of 60% and 40%, respectively. The developed non-linear regression model applied on the testing samples was capable of predicting the yield of the total viable bacteria counts with better accuracy as the determination coefficient, mean absolute error, and p-value were 0.959, 0.180, and 0.602, respectively, as opposed to the prediction with the multiple linear regression model that yielded 0.920, 0.206, and 0.514, respectively. The 2D multi-contour surface plots derived from the developed models were used to simulate the variation in the desired targets to each predictor while the others were held constant.

Rooftop photovoltaics (PV) systems are attracting residential customers due to their renewable energy contribution to houses and to green cities. However, customers also need a comprehensive understanding of system design configuration and the related energy return from the system in order to support their PV investment. In this study, the rooftop PV systems from many high-volume installed PV systems countries and regions were collected to evaluate the lifetime energy yield of these systems based on machine learning techniques. Then, we obtained an association between the lifetime energy yield and technical configuration details of PV such as rated solar panel power, number of panels, rated inverter power, and number of inverters. Our findings reveal that the variability of PV lifetime energy is partly explained by the difference in PV system configuration. Indeed, our machine learning model can explain approximately 31 % ( 95 % confidence interval: 29−38%) of the variant energy efficiency of the PV system, given the configuration and components of the PV system. Our study has contributed useful knowledge to support the planning and design of a rooftop PV system such as PV financial modeling and PV investment decision.

Wind power forecasting is a key role for large-scale wind power penetration on conventional electric power systems by understanding stochastic nature of winds. This paper proposes an empirical statistical model for forecasting monthly offshore wind speeds as a function of remotely sensed sea surface temperatures over the seas around the Korean Peninsula. The model uses the optimal lagged multiple linear regression method, and predictors are characterized by mixed periodicities derived from the autocorrelation between spatially variable satellite-observed sea surface temperatures and wind speeds at all grid points over a period of about ten years (2001 to 2008). Offshore wind speeds were found to be correlated with sea surface temperatures within a seasonal range of two- to four-month lags. In particular, offshore wind speeds were closely associated with the sea surface temperature at lag 4 M, followed by lag 3 M and lag 2 M. Correlation is less at lag 1 M as compared lag 2 M, lag 3 M and lag 4 M. The results demonstrate that this approach successfully produces accurate depictions of monthly wind speeds at the gridded network. The hindcast offshore wind speeds and wind power density showed slightly improved skills compared to the seasonally varying climatology with the value of root-mean square errors, +18% and +23%, respectively. The spatial distributions of the monthly gridded wind speed and wind power density remained fairly stable from one month to another, whereas individual regions displayed slight differences in variability. The results of this study are expected to be useful in establishing guidelines for operating and managing nascent offshore farms around the Korean Peninsula.