AI: Photovoltaic Power Forecasting Using LSTM Networks
By: Sujata Maurya
Tags: Tensorflow Lstm, Python, Lstm, Forecasting, Photovoltaic Solar Panel

Solar energy is one of the most important components of alternative sources of renewable energy. Forecasting of Solar Energy Generation is important for integration with the conventional power grids.

How AI Solutions Improve Integration of Microgrids?

AI-powered control optimization can solve power quality issues. AI algorithms have the potential to provide real-time control over the dynamic nature of the grid below the substation autonomous controls. AI Algorithms that are available can also help to improve system optimization as new devices and new generation sources are integrated.

As we move toward an increasingly electric world, more energy will be produced by decentralized, renewable sources.

1. Microgrids

2. Wind farms

3. Private solar panels, and

4. Batteries.

Solar power forecasting techniques

With AI systems predicting a building’s thermal energy demand to produce heating and cooling at the correct times through optimization of home solar and battery systems. Efficiency gains are combined with load shifting to times when electricity is cheapest, with renewable electricity available in the system.

Information about data used for the solar power generation forecast usually includes the Sun´s related data, like atmospheric conditions, the radiation of light, and the characteristics of the solar energy plant.

Generally, the solar power forecasting techniques depend on the forecasting horizon

Very short term forecasting (forecasting 3–4 hours ahead),

Short-term forecasting (up to seven days ahead)

Long-term forecasting (weeks, months, years)

The forecasting approaches can be classified into the following ways

Let's build a simple Neural Network just in a few steps —

LSTM Networks

Long Short Term Memory networks called LSTMs are a special kind of RNN, capable of learning long-term dependencies. They work well on a large variety of problems and are now widely used.

LSTMs are designed to avoid the long-term dependency problem. Remembering information for long periods of time is practically their default behavior, not something they struggle to learn!

All recurrent neural networks have the form of a chain of repeating modules of a neural network. In standard RNNs, this repeating module will have a very simple structure, such as two layers.

Prepare data in 3 dimensional

// Reshape data into [samples, timesteps, features]

traint_len = len(data) - test_len test_data = series[traint_len:]

train_data = series[:traint_len] trainX = train_data[:, :-1] // features

trainY = train_data[:, -1] // predict parameter trainX = trainX.reshape((trainX.shape[0], 1, trainX.shape[1])) testX = test_data[:, :-1] // features

testY = test_data[:, -1] // predict parameter trainX = testX.reshape((trainX.shape[0], 1, trainX.shape[1]))

Initialize model and compile

from tensorflow.keras.layers import LSTM, Dense, Dropout

from tensorflow.keras import Sequential model = Sequential()

model.add(LSTM(128, activation=’relu’, input_shape=(trainX.shape[1], trainX.shape[2]), return_sequences=True))

model.add(LSTM(32, activation=’relu’, return_sequences=False))

model.add(Dense(1))

model.compile(optimizer=’adam’, loss=’mse’)

model.summary()

Train the model

train_history = model.fit(trainX, trainY, epochs= 20, validation_data=(testX, testY))

Evaluate

yhat = model.evaluate(testX, testY)

Predict

yhat = model.predict(testX)

As a result —

Conclusion

AI provides more power to human potential. Many renewable energy suppliers are investing in artificial intelligence to drive their business.

The primary goal of AI-integrated systems is to minimize the forecasting challenges and efficiently integrate renewable energy into the central energy grid.

AI can also help renewable energy suppliers to design effective strategies and policies around current energy consumption and demands.