Analysis Affect Factors of Smart Meter A PLS-SEM Neural Network

Smart electricity meters measure, control, analyze, and predict the amount of used. Do same for water gas power. Automatically svae this monitoring data to energy provider, billing tracking services. In developed countries, there has not been a consensus accept use smart meters, in addition benefits mentioned above, are many possible risks when using meters. This paper examines information technology system (IS) related factors engineering model following technical readiness such as optimism, innovation insecurity, discomfort. Accompanying that is expectation meter, Vietnamese people’s intention continuously The oriented approach applied evaluate continuous through survey results 500 answer samples people. We propose 2-layer research analyze user about meter system. Most previous studies on systems focused analyzing impact affecting applications, single-step Structure Equation Modeling (SEM). purpose study based Technology Acceptance Method (TAM) theory, describes Artificial Neural Network (ANN) method perform indepth analysis, yielding more accurate than SEM model. measures relationship between new technologies (optimision, innovation, discomfort, insecurity). acceptance (Perceived ease use, Perceived usefulness). Expectations confirmed Information (service quality, quality). outlines multi-analysis by combining Partial Least Squares (PLS-SEM) analysis. First, PLS-SEM evaluates Second, ANN ranks important predictors from findings confirm obtained ANN. addition, performs linear non-linear relational modeling with high prediction accuracy compared an Importance Performance Map Analysis (IPMA) analyzes accurately factors’ performance.