Abstract: Multi-step forecasting for lighting and equipment energy consumption is important for fine management of building energy，regulation of power load and other areas related to building energy saving． However，due to the uncertainty，randomness and nonlinearity caused by multiple factors，such as indoor human behavior，external en-vironment and relative humidity，it is difficult to make accurate prediction of lighting and equipment energy con-sumption． In this paper，the distribution tendency of time series of sub-item energy consumption in large-scale of-fice building was analyzed，and a multi-step forecasting method for lighting and equipment energy consumption was put forward based on long-short term model． Moreover，parameter selection issues concerning the deep learning model，such as the number of hidden layer，the number of hidden layer neurons and the times of iterations depth were discussed，and the influence of sample size on the model accuracy was investigated． Simulation results show that the average accuracy of the 24h multi-step forecasting model based on deep learning is improved by 13. 25% and 4. 23% respectively compared with that of the BP neural network and least squares support vector machine．

Key words: lighting and equipment energy consumption, multi-step forecasting, deep learning, long-short term memory model, large-scale office building