Electronic Waste Management with Numerical Forecasting Models

Abstract

This research aims on the data collection of electronic waste in case of light bulb and dry battery in 6 building locations on green community of the Kasetsart university from 2016 to 2020 though these 5 years. The statistical assessment from the data collection case to forecasting with numerical modelling of the Moving average, the Weight moving average, the Simple exponential smoothing and the Holt’s exponential smoothing. The correlation between quantity of light bulb and dry batter y in 5 years from year 2016 to 2020, the correlation between the quantity e-waste is 0.1127 which mean weak correlation or almost no correlation. The prediction for dry battery in 2021 stay around 0–5 units. The forecasting e-waste for women’s dormitory location in 2021 indicate that Both of prediction for light bulb and dry batter for women’s dormitory location in 2021 are stable, but only the prediction in May and September are high. The prediction in light bulb for research institute in 2021 is high in the last 3 months of 2021, but Holt’s Exponential Smoothing has high prediction’s line in first 4 months then stable until September then the prediction’s line rises around 10 units. The prediction in dry battery in 2021 is high in January and August then stay under 10 units in 2021.  The research result benefits from Pearson’s correlation and the forecasting techniques of this research with the Moving average, the Weight moving average, the Simple exponential smoothing, the Holt’s exponential smoothing can be used to prepare for what will happen in the future, gain the valuable in insight, and thee result from prediction methods could decrease cost for the environmental management on the green community on the e-waste.