Energy has been generated based on forecasts of peak and low demands. Shallow models showed the lowest Mean Absolute Percentage Error (MAPE) A potential decrease of up to 23% in peak energy demand was found.
It is crucial today that economies harness renewable energies and integrate
them into the existing grid. Conventionally, energy has been generated based on
forecasts of peak and low demands. Renewable energy can neither be produced on
demand nor stored efficiently. Thus, the aim of this paper is to evaluate Deep
Learning-based forecasts of energy consumption to align energy consumption with
renewable energy production. Using a dataset from a use-case related to
landfill leachate management, multiple prediction models were used to forecast
energy demand.The results were validated based on the same dataset from the
recycling industry. Shallow models showed the lowest Mean Absolute Percentage
Error (MAPE), significantly outperforming a persistence baseline for both,
long-term (30 days), mid-term (7 days) and short-term (1 day) forecasts. A
potential decrease of up to 23% in peak energy demand was found that could lead
to a reduction of 3,091 kg in CO2-emissions per year. Our approach requires low
finanacial investments for energy-management hardware, making it suitable for
usage in Small and Medium sized Enterprises (SMEs).