Improving efficiency of building energy management: Prediction electricity demand and photovoltaic power generation

Abstract

CO₂ emissions from commercial and other sectors (offices, commercial facilities, and other buildings) account for about 20% of Japan’s total emissions and reducing them is an urgent issue. In this study, we developed a prediction model for electricity demand and photovoltaic power generation for a building, in order to improve the efficiency of energy management. The model was developed with LSTM, one of deep learning. In this model, following variables are used as explanatory: weather, human flow, periodic components, solar position, etc. The model predicted electricity demand and solar power generation 34 hours into the future with a correlation coefficient of over 0.85 with actual values. We confirmed an annual reduction of over 15% in electricity charges, effective use of storage batteries, the leveling of electricity demand, and effective use for disaster response are possible, through the simulation using the prediction values.