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Electricity is a volatile power source that requires great planning andresource management for both short and long term. More specifically, in theshort-term, accurate instant energy consumption forecasting contributes greatlyto improve the efficiency of buildings, opening new avenues for the adoption ofrenewable energy. In that regard, data-driven approaches, namely the ones basedon machine learning, are begin to be preferred over more traditional ones sincethey provide not only more simplified ways of deployment but also state of theart results. In that sense, this work applies and compares the performance ofseveral deep learning algorithms, LSTM, CNN, mixed CNN-LSTM and TCN, in a realtestbed within the manufacturing sector. The experimental results suggest thatthe TCN is the most reliable method for predicting instant energy consumptionin the short-term.

Deep Learning for Short-term Instant Energy Consumption Forecasting in the Manufacturing Sector