Every year, Brazil has been registering a significant growth in the number of distributed generation connections in the distribution grid due to the granting of government incentives that allowed the use of solar photovoltaic panels to spread, the most used source of energy in distributed generation in Brazil. In electrical systems with high penetration of intermittent sources, the prediction of the behavior of the load curve tends to represent a great challenge for system operators due to the unpredictability associated with power generation, which can directly impact the planning and operation of the electrical grid. To deal with this challenge, this work proposes a load forecasting methodology using recurrent neural networks with LSTM architecture, considering the impact of the distributed photovoltaic solar generation connected to the Brazilian distribution grid. Load forecasts are made for the Brazilian National Interconnected System and for the subsystems that integrate it, taking into account a short-term horizon, of 24 hours, in hourly intervals, and a medium-term horizon, of 60 months, in monthly intervals. The results indicate that the methodology can be an efficient tool for obtaining load forecasts and can also be used for different forecast horizons than those presented in this work. The MAPE found for short-term forecasts is no more than 2 percent and for medium-term forecasts, no more than 3.5 percent.