Laboratory prediction energy control system based on artificial intelligence network

Abstract

The use of electrical energy increases globally every year. The laboratory prediction energy control system (LPECS) predicted energy demand. This research was conducted in the Electrical Engineering Vocational Education laboratory by comparing the artificial neural fuzzy system (ANFIS) with the fuzzy logic. The comparison of methods aimed to determine their reliability in the energy demand prediction systems. The results showed that the minimum value of the target data using the conventional method (actual data) was 44.42%. Meanwhile, the prediction data using the ANFIS method was 44.33%, and the prediction data using the fuzzy method was 59.31%. The maximum value of the conventional ways (actual data) of ANFIS and fuzzy was similar by 77.59%. The RMSE ANFIS value was 0.1355%, the mean absolute percentage error (MAPE) was 0.2791%, and the fuzzy logic was 0.1986%. Thus, the ANFIS is applicable to determine the minimum and maximum values. Meanwhile, fuzzy can only show the maximum value but cannot reach the minimum value properly.

Simulation of Automatic Speed Control in Motor Vehicles (Cruise Control) Using Simulink PID Controller

“…In the 20th century, many technologies have been developed that lead to automatic control systems. Vehicle automation systems have attracted the attention of many parties both in research and commercial terms [1], [2], [3], [4], [5] . There are a lot of technologies nowadays, one of the most common to increase driving comfort is cruise control system technology or automatic speed control [6].…”

Comparison of principal component analysis and ANFIS to improve EEVE Laboratory energy use prediction performance

“…There has been a lot of research on the benefits and importance of more in-depth studies of energy utilization and consumption in major cities in the world, including those in Jordan [12], [13], China [14], Turkey [15], [16], Iran [17], Taiwan [18], South Korea [19], European [20], and Ontorio in Canada [21]. Energy consumption in colleges is heavily influenced by the climate [22], [23]. For instance, at Guang Dong University, a study has been conducted by building a conservation-oriented campus, which has a significant effect on energy saving.…”