Forecasting Energy Consumption using Machine Learning

Managing electrical energy consumption is crucial, simply because of one fact: Electricity cannot be stored, unless converted to other forms. It is best for produced electricity to be instantly consumed; otherwise, additional resources and costs are incurred to store convert and store the excess energy. Energy-efficient buildings provide both economic and environmental benefits, maximising profits and social welfare. Conversely, underestimating energy consumption could be fatal, with excess demand overloading the supply line and even causing blackouts, leading to operational downtime. Clearly, there are tangible benefits in closely monitoring the energy consumption of buildings -- be they office, commercial or household. With the advent of machine learning and data science, accurately predicting future energy consumption becomes increasingly possible. This provides two-fold benefits: firstly, managers gain key insights into factors affecting their building's energy demand, providing opportunities to address them and improve energy efficiency. Not only that, forecasts provide a benchmark to single out anomalously high/low energy consumption and alert managers to faults within the building.