Statistics

Electrical engineering

Naive Bayes classifier

Biology

Support vector machine

Machine learning

Time series

Paleontology

Multilayer perceptron

Data stream mining

Data mining

Computer science

Cluster analysis

Artificial intelligence

Energy consumption

Artificial neural network

Energy (signal processing)

Context (archaeology)

Big data

Engineering

Mathematics

Perceptron

Energies

Responsible, efficient and environmentally aware energy consumption behavior is becoming a necessity for the reliable modern electricity grid. In this paper, we present an intelligent data mining model to analyze, forecast and visualize energy time series to uncover various temporal energy consumption patterns. These patterns define the appliance usage in terms of association with time such as hour of the day, period of the day, weekday, week, month and season of the year as well as appliance-appliance associations in a household, which are key factors to infer and analyze the impact of consumers’ energy consumption behavior and energy forecasting trend. This is challenging since it is not trivial to determine the multiple relationships among different appliances usage from concurrent streams of data. Also, it is difficult to derive accurate relationships between interval-based events where multiple appliance usages persist for some duration. To overcome these challenges, we propose unsupervised data clusteri...

Big Data Mining of Energy Time Series for Behavioral Analytics and Energy Consumption Forecasting