...Gartrude Miriam Morara; Elisha Makori Ondieki Purpose This study aims to assess how advanced machine learning can enhance renewable energy forecasting accuracy in Kenya, reduce dependence on expensive diesel backup, lower electricity costs and cut carbon emissions. It compares modern machine...

... is analyzed for all the selected companies. In the next step, the companies are classified using K-means++ clustering, an unsupervised machine learning method. Subsequently, the influence of ESGD on FP is assessed for the companies within different clusters. To the author’s knowledge, this study is the first...

..., augmented reality, machine learning and Internet of Things were identified as useful methods for the upgradation of the existing geothermal energy system. Research limitations/implications If proposed recommendations are incorporated, the efficiency of geothermal energy generation will increase...

... forecasting Autoregressive Neural networks Fuzzy-logic model Demand-side management Gasoline Liquid fuels Fuel demand Forecasting methods Time series Machine learning Forecast evaluation The management of energy demand has become a mandatory issue for public and private agents working...

... and assessing the eco-efficiency determining factor in Iran’s 22 local cement companies over 2015–2019. Design/methodology/approach This research uses two well-known artificial intelligence approaches, namely, optimization data envelopment analysis (DEA) and machine learning algorithms at the first...

... methodology selects and applies machine learning techniques such as decision trees, support vector machines and ensemble learning to predict customer satisfaction from service data, power outage data and reliability indices. Findings The results on the predicted main indicator diverged only by 1.36 per...

... (MCMC) sampling techniques. Machine learning tools are used to calibrate the values of the HW model parameters. Hybridization is conducted to reduce modeling uncertainty. The technique is applied to real load data. Monthly peak load forecasts are calculated as weighted averages of HW and MCMC estimates...