Howard Okezie Njoku

Accurate measurements of solar radiation are essential for photovoltaic (PV) and other solar energy applications. However, the high cost of standard thermopile pyranometers limits their deployment in distributed monitoring networks. In this study, a low-cost hybrid sensor combining a BH1750 optical sensor and dual thermistors with an ESP32 was developed. Using more than 53,000 field observations validated against a Class-A reference pyranometer, 40 engineered features were generated, of which 26 were retained using LASSO selection, out of which a ratio of optical and thermal corresponding values made up 88.15% of the importance magnitude. Four machine-learning algorithms Generalised Linear Regression (GLR), Random Forest (RF), Multilayer Perceptron Neural Network (MLPNN), and Gradient Boosting (GB) were evaluated. GB achieved the best overall performance, with (R² = 0.9986), RMSE = 10.60 W/m², and nRMSE = 2.30%, while RF provided the lowest mean absolute error (MAE = 4.23 W/m²) with (R² = 0.9985) and RMSE = 10.84 W/m²). Compared with the single-sensor baseline, the ensemble models reduced prediction error by approximately 80%. The prototype cost approximately $22.8 per unit, representing a potential 96% reduction relative to the Class-A reference pyranometer. These results demonstrate that combining low-cost optical and thermal measurements with machine-learning can achieve high-accuracy irradiance estimation while substantially reducing the cost.

As conventional exergy analyses do not reveal the exergy destruction rates in a thermal system component caused by inefficiencies of interconnected components, actual potentials for improving the component performances cannot be provided by such analyses. This study analyses a combined-cycle gas turbine power plant using advanced exergy analysis methodologies, which address the shortcomings of conventional exergy analysis by evaluating the exergy destruction rates that are endogenous and exogenous, avoidable and unavoidable. Avoidable exergy destruction rates in the entire plant were found to be 31% of the total exergy destruction rates, indicating a significant potential for improving the plant. Exergy destruction rates for most of the plant components were largely endogenous (95.2%), signifying that contributions of cross-component interactions were limited. Avoidable endogenous exergy destruction rates account for 28.4% of the overall exergy destruction rates in the plant, while avoidable exogenous exergy destruction rates account for 2.1%. A component-level ranking of the plant components ranked the pumps in the plant as first for improvement whereas the highest priority was allocated to the combustion chambers (CC) by a plant-level ranking. A parametric study of the influence of CC operating conditions on the plant’s performance showed that CC operating temperatures more significantly affected plant exergy destruction rates than the CC operating pressures.

The benchmarking of hotel energy use comprehensively identifies the controllable and uncontrollable factors affecting energy performance, including building characteristics, management strategies, operations, and maintenance systems. Other factors include climatic conditions, floor areas, operating hours, occupancy rates, and guest populations. A benchmarking study on energy consumption patterns in significant hotels (each with less than 100 rooms and an average staff strength of 40 employees), situated in the university town of Nsukka (longitude 70 23′ E, latitude 60 52′ N), Nigeria, was performed using the data envelopment analysis (DEA) methodology. The DEA, a linear programming technique that measures the relative performances of units, was chosen as a benchmarking methodology due to its ability to handle multiple inputs and outputs. Following a correlation test, energy use intensity, diesel consumption, and the number of employees were selected as the analysis inputs, while the occupancy rate was chosen as the output variable. Data on these variables spanning 12 months were collected using questionnaires, interviews, site visits, and oral conversations with hotel managers to ensure validity. Grid-supplied electricity accounted for most of the hotels’ energy needs, followed by diesel used in generators. More than 70% of the electricity use was for HVAC. From the DEA, Hotel 3 (DMU H3) had a technical efficiency score of 1, whereas adjustments were recommended for improving the efficiency scores of the other hotels, which were deemed inefficient. DMU H7 had the lowest efficiency score (0.474) and the highest identified savings for electricity and diesel. The analysis also revealed that occupancy rates were generally low in the months of June and July, coinciding with the high rainfall season with its accompanying decline in outdoor activities. Consistent with this, electricity consumption was highest in the Christmas and Easter holiday months of December, January, and April following increased travel-related activities.

The benchmarking of hotel energy use comprehensively identifies the controllable and uncontrollable factors affecting energy performance, including building characteristics, management strategies, operations, and maintenance systems. Other factors include climatic conditions, floor areas, operating hours, occupancy rates, and guest populations. A benchmarking study on energy consumption patterns in significant hotels (each with less than 100 rooms and an average staff strength of 40 employees), situated in the university town of Nsukka (longitude 70 23' E, latitude 60 52' N), Nigeria, was performed using the data envelopment analysis (DEA) methodology. DEA, a linear programming technique that measures the relative performances of units, was chosen as a benchmarking methodology due to its ability to handle multiple inputs and outputs. Following a correlation test, energy use intensity, diesel consumption, and the number of employees were selected as the analysis inputs, while the occupancy rate was chosen as the output variable. Data on these variables spanning 12 months were collected using questionnaires, interviews, site visits, and oral conversations with hotel managers to ensure validity. Grid-supplied electricity accounted for most of the hotels' energy needs, followed by diesel used in generators. More than 70% of the electricity use was for HVAC. From the DEA, Hotel 3 (DMU H3) had a technical efficiency score of 1, whereas adjustments were recommended for improving the efficiency scores of the other hotels, which were deemed inefficient. DMU H7 had the lowest efficiency score (0.474) and the highest identified savings for electricity and diesel. The analysis also revealed that occupancy rates were generally low in the months of June and July, coinciding with the high rainfall season with its accompanying decline in outdoor activities. Consistent with this, electricity consumption was highest in the Christmas and Easter holiday months of December, January, and April following increased travel-related activities.

The advanced exergy and exergoeconomic analysis methodologies excel over their conventional counterparts by quantifying endogenous/exogenous exergy destruction rates and related costs that reveal the interactions among system components. By quantifying avoidable/unavoidable losses, they also reveal the potentials for component improvements that are practically achievable. These data are imperative for improving the cost-effectiveness of thermal systems. This paper presents the performances of a complex gas-fired power plant obtained via an advanced exergoeconomic analysis. The study advances existing studies by undertaking second-tier splitting of exergy destruction cost rates and associated investment cost rates to determine avoidable endogenous, unavoidable endogenous, avoidable exogenous, and unavoidable exogenous cost rates of the plant’s components. Component exergy destruction cost rates were found to be predominantly unavoidable, while most of the component investment cost rates were avoidable. Except for the low pressure heater 3, exergy destruction cost rates are endogenous in all the plant components, contributing 84% of overall cost rates. The proportions of exergy destruction cost rates and investment cost rates of the plant that are avoidable endogenous were 21% and 28% respectively, while the respective portions that are avoidable exogenous were 4% and 27%. Furthermore, it was shown that as much as 96.3% improvements in overall plant cost-effectiveness were achievable by eliminating avoidable endogenous exergy destruction cost rates and investment cost rates for major components of the plant.