Predicting Household Electric Power Consumption Using Multi-step Time Series with Convolutional LSTM

Lucia Cascone, Saima Sadiq, Saleem Ullah, Seyedali Mirjalili, Hafeez Ur Rehman Siddiqui, Muhammad Umer

Research output: Contribution to journalArticlepeer-review

10 Citations (Scopus)


Energy consumption prediction has become an integral part of a smart and sustainable environment. With future demand forecasts, energy production and distribution can be optimized to meet the needs of the growing population. However, forecasting the demand of individual households is a challenging task due to the diversity of energy consumption patterns. Recently, it has become popular with artificial intelligence-based smart energy-saving designs, smart grid planning and social Internet of Things (IoT) based smart homes. Despite existing approaches for energy demand forecast, predominantly, such systems are based on one-step forecasting and have a short forecasting period. For resolving this issue and obtain high prediction accuracy, this study follows the prediction of household appliances' power in two phases. In the first phase, a long short-term memory (LSTM) based model is used to predict total generative active power for the coming 500 hours. The second phase employs a hybrid deep learning model that combines convolutional characteristics of neural network with LSTM for household electrical energy consumption forecasting of the week ahead utilizing Social IoT-based smart meter readings. Experimental results reveal that the proposed convolutional LSTM (ConvLSTM) architecture outperforms other models with the lowest root mean square error value of 367 kilowatts for weekly household power consumption.

Original languageEnglish
Article number100360
JournalBig Data Research
Publication statusPublished - 28 Feb 2023


  • Convolutional neural network
  • Deep learning
  • Long short term memory
  • Multi-step forecasting
  • Power consumption
  • Social IoT


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