Probabilistic LSTM-Autoencoder Based Hour-Ahead Solar Power Forecasting Model for Intra-Day Electricity Market Participation: A Polish Case Study

Abstract

This article presents the selection of an appropriate deep learning Long Short-Term Memory (LSTM) based probabilistic hour-ahead forecasting model for a grid connected industrial solar PV power plant located in Poland. It has a 317 kW peak power capacity and is connected with a metallurgical plant producing steel for car parts. The purpose of the study is to present a model that could be used by the plant to participate in the Polish intra-day electricity market. Four different LSTM models were investigated which include the vanilla model, the stacked LSTM model, the Bi-directional LSTM model and the LSTM-Autoencoder model. Out of the investigated models it was observed that the LSTM-Autoencoder model was the best performing one in terms of reliability. The average Root Mean Squared Error (RMSE) and the average Mean Absolute Error (MAE) for the Autoencoder model over 100 runs were 15.59 kW and 8.36 kW which represent 4.9% and 2.6% of the peak power respectively. Moreover, it was observed that it has the shortest width for the 95% confidence interval of only 0.5% for both the RMSE and the MAE. In terms of accuracy the best performing model was the LSTM bi-directional model with the average RMSE and MAE values of 12.87 kW and 6.91 kW which represent 4% and 2.1% of the peak power. The 95% confidence intervals width for both the RMSE and MAE over the 100 runs were 0.8% and 0.5% respectively.INDEX TERMS Solar power forecasting, LSTM, deep learning, electricity market and grid connected solar PV.

Design of an Iterative Method for Optimizing Solar Power Systems using Quad LSTM with IoT Integration Operations

“…The objective is to discover a policy π that maximizes the expected cumulative reward. The Q-function is iteratively updated according to the Bellman process via equation 13, Qnew(s,a)=Q(s,a)+α[r+γmaxa^' Q(s^',a^' )-Q(s,a)]… (13) Where, α is the learning rate, r the immediate reward, γ the discount factor, and s′ the new state after action a is taken. The term maxa′Q(s′,a′) reflects the maximum expected utility achievable from the new state, embodying the essence of future reward prospects.…”

“…However, the validation of this model is limited to specific weather conditions, potentially constraining its generalizability to diverse environmental settings. Suresh et al [13] proposed a Probabilistic LSTM-Autoencoder for hour-ahead solar power forecasting in electricity markets. While their model improved forecasting accuracy, its focus on a specific market may limit its broader applicability to other regions or energy markets.…”

Load Forecasting for the Laser Metal Processing Industry Using VMD and Hybrid Deep Learning Models

“…To evaluate the predictive models, test data were input into the models to generate predictions. The accuracy of the predictions was measured using three error metrics [40,46]: root mean square error (RMSE), which is presented in Equation ( 17), mean absolute error (MAE), as shown in Equation ( 18), and the mean absolute percentage error (MAPE) in Equation (19). The RMSE measures the spread of prediction errors [14,46], while the MAE calculates the average magnitude of prediction errors [33,35], and the MAPE measures the average percentage difference between the predicted value and actual value [47,48].…”

Enhanced Forecasting Accuracy of a Grid-Connected Photovoltaic Power Plant: A Novel Approach Using Hybrid Variational Mode Decomposition and a CNN-LSTM Model

“…Many works on various DL methods for forecasting solar power have been published in the literature. Some examples of these approaches include the gated recurrent unit (GRU) [6], the long shortterm memory network (LSTM) [7][8][9][10], and the convolutional neural network (CNN) [11][12][13].…”