# Time series forecasting LSTM small dataset

I'm new to machine learning, i'm trying to use LSTM to forecast the power production of a solar power plant, i have a small dateset that contains 7200 rows and 4 columns, i divided the data into training data (70%) and validation(30 %) and i didn't use a test set yet as i'm still experimenting, here are the configurations of my model :

generator = TimeseriesGenerator(dataset[:5065, :], dataset[:5065, :1], length=n_input, batch_size=16)
valgen = TimeseriesGenerator(dataset[5066:, :], dataset[5066:, :1], length=n_input, batch_size=16)

# define model
model = Sequential()
model.add(LSTM(500, activation='relu', input_shape=(n_input, n_features), return_sequences=True))
model.add(Dense(100, activation='relu', input_shape=(n_input, n_features)))