Development Of An Automatic Can Crusher Using Programmable Logic Controller

The invention of a can crusher machine in this project is to reduce the wasted storage space occupied by the tremendous amount of use aluminium can at the commercial establishment like in the restaurant, cafeteria and bar. Basically, can crusher machine be operated in manual effort and time in the can crushing process. Shrinking the initial volume of empty used-aluminium cans down to 50% in more effective, faster and effortless way, as well as to develop a low-cost device that is suitable for the small-industry usage are mainly the objectives for the Automatic Can Crusher, where an automated process is executed in Automatic Can Crusher due to the automation in the modern world is inevitable and nominal to be used. The Automatic Can Crusher is run by a Programmable Logic Controller (PLC) with the aid of an inductive and capacitive sensor, where it is applied to detect whether the object is metal or non-metal. Overall, the system can be controlled manually through the push start and stop button as well as using the Human Machine Interface (HMI) using NB-Designer, for displaying the total of cans being crushed per day. The average result of empty can could shrink from 31% to 60 % of the original value, by using the attuned and compatible pressure for this system

Prediction of energy consumption in campus buildings using long short-term memory

In this paper, Long Short-Term Memory (LSTM) was proposed to predict the energy consumption of an institutional building. A novel energy usage prediction method was demonstrated for daily day-ahead energy consumption by using forecasted weather data. It used weather forecasting data from a local meteorological organization, the Malaysian Meteorological Department (MET). The predictive model was trained by considering the dependencies between energy usage and weather data. The performance of the model was compared with Support Vector Regression (SVR) and Gaussian Process Regression (GPR). The experimental results with a dataset obtained from a building in Multimedia University, Malacca Campus from January 2018 to July 2021 outperformed the SVR and GPR. The proposed model achieved the best RMSE scores (561.692–592.319) when compared to SVR (3135.590–3472.765) and GPR (1243.307–1334.919). Through experimentation and research, the dropout method reduced overfitting significantly. Furthermore, feature analysis was done with SHapley Additive exPlanation to identify the most important weather variables. The results showed that temperature, wind speed, rainfall duration and the amount had a positive effect on the model. Thus, the proposed approach could aid in the implementation of energy policies because accurate predictions of energy consumption could serve as system fault detection and diagnosis for buildings

Prediction of energy consumption in campus buildings using long short-term memory

In this paper, Long Short-Term Memory (LSTM) was proposed to predict the energy consumption of an institutional building. A novel energy usage prediction method was demonstrated for daily day-ahead energy consumption by using forecasted weather data. It used weather forecasting data from a local meteorological organization, the Malaysian Meteorological Department (MET). The predictive model was trained by considering the dependencies between energy usage and weather data. The performance of the model was compared with Support Vector Regression (SVR) and Gaussian Process Regression (GPR). The experimental results with a dataset obtained from a building in Multimedia University, Malacca Campus from January 2018 to July 2021 outperformed the SVR and GPR. The proposed model achieved the best RMSE scores (561.692–592.319) when compared to SVR (3135.590–3472.765) and GPR (1243.307–1334.919). Through experimentation and research, the dropout method reduced overfitting significantly. Furthermore, feature analysis was done with SHapley Additive exPlanation to identify the most important weather variables. The results showed that temperature, wind speed, rainfall duration and the amount had a positive effect on the model. Thus, the proposed approach could aid in the implementation of energy policies because accurate predictions of energy consumption could serve as system fault detection and diagnosis for buildings