Sensitivity Analysis of Time Length of Photovoltaic Output Power to Capacity Configuration of Energy Storage Systems

Wang, Mingqi; Zheng, Xinqiao

doi:10.3390/en10101616

Cited by 1 publication

(2 citation statements)

References 24 publications

(23 reference statements)

Supporting

Mentioning

Contrasting

Order By: Relevance

“…Moreover, the methodology proposed in [21] extracts electric energy consumption patterns in big-data time series, to draw valuable conclusions for managers and governments. Authors in [22] propose a methodology to determine the appropriate time interval and time length for the analysis, based on the weather characteristics, clustering analysis methods and statistical principles.…”

Section: Related Workmentioning

confidence: 99%

“…• Date, holiday (yes or no), week of the year (1-52), month (1-12), 2-month, 3-month, 4-month, 6-month periods; • Daily time slots, i.e., morning [4][5][6][7][8], midday [9][10][11][12][13], afternoon [14][15][16][17], evening [18][19][20][21][22]; during the night, from 22 to 4, the heating system is switched off in the buildings under study.…”

Section: Data Descriptionmentioning

confidence: 99%

See 1 more Smart Citation

METATECH: METeorological Data Analysis for Thermal Energy CHaracterization by Means of Self-Learning Transparent Models

2018

View full text Add to dashboard Cite

In the last few years, a large number of smart meters have been deployed in buildings to continuously monitor fine-grained energy consumption. Meteorological data deeply impact energy consumption, and an in-depth analysis of collected and correlated data can uncover interesting and actionable insights to improve the overall energy balance of our communities and to enhance people's awareness of energy wasting. To effectively extract meaningful and interpretable insights from large collections of energy measurements and multi-dimensional meteorological data, innovative data science methodologies should be devised. Research frontiers are addressing self-learning approaches, which allow non-experts to exploit machine learning techniques more easily, and algorithmic transparency of models, hence providing actionable, explicit, declarative knowledge representation. This paper presents METeorological Data Analysis for Thermal Energy CHaracterization (METATECH), a data mining engine based on both exploratory and unsupervised data analytics algorithms, devised to build transparent models correlating weather conditions and energy consumption in buildings. METATECH exploits a joint approach coupling cluster analysis and generalized association rules to allow a deeper yet human-readable understanding of how meteorological data impact heating consumption. First, a partitional clustering algorithm is applied to weather conditions. Then, resulting clusters are characterized by means of generalized association rules, which provide a self-learning explainable model of the most interesting correlations between energy consumption and weather conditions at different granularity levels. The experimental evaluation performed on real datasets demonstrates the effectiveness of the proposed approach in automatically extracting interesting knowledge from data, and provide it transparently to domain experts.

show abstract

Section: Related Workmentioning

confidence: 99%