This project aims to predict the energy consumption by home appliances. With the advent of smart homes and rising need for energy management, existing smart home systems can benefit from accurate prediction. If the energy usage can be predicted for every possible state of appliances, then device control can be optimized for energy savings as well. I am implementing the prediction model by using methods of Deep Learning. Data used include measurements of temperature and humidity sensors in a time series format from a wireless network, weather from nearby airport station and recorded energy use of lighting fixtures Appliance energy usage is the target variable while sensor data and weather data are the features. The method discusses data filtering to remove non-predictive parameters and feature ranking. From the wireless network, the data from the kitchen, laundry and living room were ranked the highest in importance for the energy prediction. The prediction models with only the weather data, selected the atmospheric pressure (which is correlated to wind speed) as the most relevant weather data variable in the prediction. Therefore, atmospheric pressure may be important to include in energy prediction models and for building performance modelling. Some other features included in my model which are time date and weekdays.