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.
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