{'fraction_non_alphanumeric': 0.01722488038277512, 'fraction_numerical': 0.0028708133971291866, 'mean_word_length': 5.41717791411043, '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}}
On the effects of MPC on a domestic energy efficiency optimization methodology

Increasing energy prices and the greenhouse effect lead to more awareness of energy efficiency of electricity supply. During the last years, a lot of technologies and optimization methodologies were developed to increase the efficiency, maintain the grid stability and support large scale introduction of renewable sources. In previous work, we showed the effectiveness of our three-step methodology to reach these objective, consisting of 1) offline prediction, 2) offline planning and 3) online scheduling. Although initial results are promising, one of the problems of the current implementation is the inability to work around prediction errors in the last step. Therefore, we added Model Predictive Control to step three to incorporate future states in the control to work around prediction errors. Adding MPC improves the ability to work around prediction errors and especially improves the irregular behavior of devices, resulting in a more stable situation.