Accurate forecasting of electricity load is essential for electricity companies, primarily for planning electricity generators. Overestimated or underestimated forecasting value may lead to inefficiency of electricity generator or electricity deficiency in the electricity grid system. Parameters that may affect electricity demand are the weather conditions at the location of the electricity system. In this paper, we investigate possible weather parameters that affect electricity load. As a case study, we choose an area with an isolated electricity system, i.e., Bali Island, in Indonesia. We calculate correlations of various weather parameters with electricity load in Bali during the period 2018–2019. We use two machine learning models to design an electricity load forecasting system, i.e., the Generalized Regression Neural Network (GRNN) and Support Vector Machine (SVM), using features from various weather parameters. We design scenarios that add one-by-one weather parameters to investigate which weather parameters affect the electricity load. The results show that the weather parameter with the highest correlation value with the electricity load in Bali is the temperature, which is then followed by sun radiation and wind speed parameter. We obtain the best prediction with GRNN and SVR with a correlation coefficient value of 0.95 and 0.965, respectively.

This study aimed to examine the customer interest in using rooftop PV considering the economic background and customer profile in Indonesia’s electricity market using primary survey data with potential and existing (households and industries) respondents. This research uses logit model regression to analyze the impact of the demographic background of respondents and uses exploratory factor analysis (EFA) to understand the reasons why the existing users utilize rooftop PV at their homes. The results show that education, residence location, and income can positively and significantly affect the probability of using rooftop PV as the source of electricity. Then, there are several factors that influence the use of rooftop PV, such as easily finding it in their area, having concern for the environment, following trends, and loyalty. Some disadvantages of installing rooftop PV are felt by users, such as relatively high installation cost and frequent overheating during usage. Regarding customer satisfaction, most of the respondents from both households and industries answered that they were satisfied with their rooftop’s PV. Consumers say that the benefits they obtain are comparable to the required installation costs, and the majority of consumers also said that the rooftop PV worked well and did not need many repairs every month, so consumers did not need to spend significant money on it.

A journal bearing is a machine element that is used to keep the shaft rotating about its axis. The increasing demand for journal bearing applications in high-speed machines that are efficient and economical has resulted in the need for improvements to the acoustic and tribological performance of journal bearings. In order to improve the tribological and acoustic performance, this study aims to propose a novel journal bearing design by introducing a roughness condition in a specific zone of the stationary bearing surface. In addition, the impact of the application of engineered roughness on the performance of Bingham-plastic-lubricated bearings is investigated in more detail. Considering the effect of cavitation, the analysis was conducted using a 3D computational fluid dynamics (CFD) model of a journal bearing. In comparison with the Reynolds equation—which is inertialess—for lubrication analysis, the use of a 3D CFD model based on Navier–Stokes equations reflects more detailed flow characteristics. Moreover, in this work, variations in the area of surface roughness were employed, resulting in various roughness patterns on the surface of the journal bearing, so that the acoustic and tribological performances of the journal bearing were anticipated to be enhanced. The findings of this study show that under non-Newtonian lubrication of the bearing, the engineered roughness has a strong effect in altering the tribological performance. Furthermore, the well-chosen roughened surface was proven to be more pronounced in enhancing the load support and reducing the friction force. The simulation results also show that using an engineered surface has little effect on the noise of the bearing.

Blockchain technology recently draws populous attention, both in academic research and industry area including energy industry. Blockchain is viewed as an innovative technology that can enhance conventional way of doing business, especially those that require intermediary involvement. Perusahaan Listrik Negara or PLN as the state owned electricity company in Indonesia also seek to improve its business process using current technology, including evaluating to implement Blockchain in PLN business processes. This study aims to develop a workable framework in identifying suitable business activity for Blockchain implementation in PLN. The framework is developed from existing frameworks with adjustment to better fit the application in PLN. The developed framework is then used in a focus group discussion involving business process stake holders to identify which business area Blockhcain can enhance the process and suitable for implementation. This study finds that certain existing PLN business process can be enriched by implementing Blockchain technology. Numbers of future innovative businesses that can help PLN to improve customer satisfaction and provide revenue to PLN also identified.