Energy distribution is often related to the local environment. Regarding energy distribution prediction, data on local environmental factors can be availed to predict the value of energy to be distributed to the region, thereby maximizing the extent of proper energy distribution. The long short-term memory (LSTM) network, despite its favorable short-term prediction effect, is weakened by error accumulation, a slow speed, and poor accuracy when it is used for long-term data prediction. As a new algorithmic energy prediction model recently proposed, Informer is fast but not sufficiently capable of prediction in this task. This study proposes a Conv1d-LSTM model that achieves better prediction results than those of the above two models with a smaller mean absolute error (MAE) and root mean square error (RMSE).