Browsing by Author "0000-0001-8886-7853"
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- ItemDecomposition and drivers of energy intensity in Ghana(Energy Strategy Reviews, 2023) Oteng-Abayie, Eric Fosu; Dramani, John Bosco; Adusah-Poku, Frank; Amanor, Kofi; Quartey, Jonathan Dagadu; 0000-0002-4598-2066; 0000-0001-8886-7853; 0000-0001-5513-4530; 0000-0002-6937-847X; 0000-0002-7333-2300Ghana’s energy intensity trends point to a high energy use necessary to generate a unit of output. The country has also witnessed massive investment in energy infrastructure geared towards meeting its lower middle-income status and achieving universal access to energy. The logical question is: what is the contribution of the cur rent economic and technical infrastructure level to the country’s energy intensity? The current study addresses this question by employing the Logarithmic Mean Divisia Index I (LMDI) to decompose energy intensity in Ghana from 2000 to 2020 to examine its trends and sources. The impact of economic-technical factors on aggregate energy intensity in Ghana is then investigated with the aid of the ARDL estimation technique to unearth potential asymmetric and symmetric effects. The decomposition analysis indicates an oscillating pattern in energy in tensity in Ghana promoted by structural effect and labour productivity respectively. The results suggest that renewable energy, rural electrification, and digitisation have a direct and secondary long-run asymmetric effect on aggregate energy intensity with labour productivity and household consumption working as the transmission channels. The study recommends the need for government to pursue clean and eco-friendly practices in its economic development agenda for a meaningful reduction in energy intensity.
- ItemMachine learning of redundant energy of a solar PV Mini-grid system for cooking applications(Solar Energy, 2023) Opoku, Richard; Adjei, Eunice A.Mensah, Gidphil; Adjei, Eunice A.; Dramani, John Bosco; Kornyo, Oliver; Nijjhar, Rajvant; Addai, Michael; Marfo, Daniel; Davis, Francis; Obeng, George Yaw; 0000-0001-8766-3402; 0000-0003-2702-6465; 0000-0002-7945-8676; 0000-0002-3640-2664; 0000-0001-8886-7853Solar PV mini-grids are increasingly being deployed in off-grid and island communities especially in sub-Saharan Africa (SSA) countries to meet household energy demand. However, one challenge of solar PV mini-grids for community energy supply is the mismatch between the PV energy generation and household energy demand. PV mini-grid energy generation is highest in the afternoon whilst household energy demand is highest in the mornings and evenings, but lowest in the afternoons. This mismatch creates redundant energy generation during peak sunshine hours when battery energy storage is full, leading to low profitability for mini-grid systems. In this study, four machine learning models have been applied on an installed 30.6 kW mini-grid system in Ghana to ascertain the level of the redundant energy. The study has revealed that redundant energy exists on the mini-grid, in the range of 56.98 – 119.86 kWh/day. Further analysis has shown that the redundant energy can support household cooking energy demand through sustainable thermal batteries. With the four machine learning (ML) models applied in predicting the redundant energy, the most accurate ML model, K-nearest Neighbour Regressor, had a root mean square error (RMSE) of 0.148 and a coefficient of determination (R2 ) value of 0.998.