{'fraction_non_alphanumeric': 0.025693730729701953, 'fraction_numerical': 0.010277492291880781, 'mean_word_length': 5.625850340136054, '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}}
Power prediction analysis using artificial neural network in MS excel

In this study, an artificial neural network (ANN) based model for prediction of solar energy potential in Kuala Lumpur, Malaysia was developed. Standard multilayered, feed-forward, back-propagation neural networks were designed using Microsoft Excel (MS Excel). The meteorological data were acquired from Malaysia Meteorological Department. The data was consists of meteorological data from one station in Subang for period of 10 years (2003–2012) from were used for the training and testing the network. Parameter of month, sunshine duration, minimum temperature, maximum temperature, average temperature and relative humidity were used as inputs to the network, while the solar radiation was used as the output of the network. Simulation result shows that ANN potentially predicts solar radiation. Furthermore this prediction can contribute an enhancement of renewable energy development in Malaysia.