{'fraction_non_alphanumeric': 0.015494636471990465, 'fraction_numerical': 0.011918951132300357, 'mean_word_length': 5.086956521739131, 'pattern_counts': {'":': 0, '<': 0, '<?xml version=': 0, '>': 0, 'https://': 0, 'lorem ipsum': 0, 'www.': 0, 'xml': 0}, 'pii_count': 0, 'substrings_counts': 0, 'word_list_counts': {'cursed_substrings.json': 0, 'profanity_word_list.json': 0, 'sexual_word_list.json': 0, 'zh_pornsignals.json': 0}}
Energy prediction of a combined cycle power plant using a particle swarm optimization trained feedforward neural network

Combined cycle power plants are frequently used for power production. Predicting the power plant output based on operational parameters is in major focus nowadays. The paper presents a novel approach using a particle swarm optimization trained feedforward neural network to predict power plant output. It takes ambient temperature, atmospheric pressure, relative humidity, and vacuum as input parameters to a feedforward neural network to predict average hourly output of the power plant. PSO is used as a learning algorithm. The MSE for training data is found to be 1.019e-04 and 0.005 for testing data. The proposed technique shows promising results to predict power plant output using a PSO trained neural network.