# A Cyber-Physical Approach for Residential Energy Management: Current State and Future Directions
## 6 Data Analytics
### Forecasting Appliance Consumption  
On the other hand, load forecasting can be performed at two different levels: grid level or appliance level. For the utility, the prediction of the load allows better management of the energy generation and distribution resources, and can inform dynamic pricing to reduce the peak demand. For the individual consumers, predicting the load allows the identification of energy loads that can be shifted to off-peak hours, therefore reducing the energy bill. In terms of control, load forecasting can be key to provide energy-efficient scheduling plans for smart homes [36]. In [33], the authors classified forecasting methods based on the prediction period:  
* Very Short-Term Load Forecast (vSTLF): Referring to forecasting the load for the next several minutes.
* Short-Term Load Forecast (STLF): Refers to load prediction for the next several hours or a week ahead.
* Medium-Term Load Forecast (MTLF): Refers to predictions made for a week or a year ahead.
* Long-Term Load Forecast (LTLF): Referring to predictions made for the next several years.  
Load forecasting is modeled as a time series problem in which ML techniques have shown promising results in recent years, especially those based on convolutional neural networks (CNNs) and long short-term memory (LSTM) networks. The input of the models are generally features which are based on the history of the householders. This means that these features can be based on historical power consumption or others more related to environmental parameters and economy. The output of the model can be either a single value or a sequence representing the future power consumption at a given time resolution (month, day, hours) [36]. In [141], the authors used continuous and categorical data of each consumer as input of three different models: feed-forward neural network (FFNN), a CNN, and an LSTM network. The model's objective is to predict monthly consumption at the grid level. The categorical data represents the months is to be predicted, the number of phases of the power and the class of the consumer (e.g., residential, industrial). The authors of [142] benchmarked a CNN and an LSTM model to estimate the peak demand at the grid level. They used historical demand records as input, along with temperature and economic index information. Moreover, in [142], the authors gave a literature review comparing the methods and resolutions used for forecasting.  
Forecasting at the appliance level can be very useful for HEMS since it allows to identify usage patterns of individual appliances, which is a key fact for scheduling and control mechanisms. However, this is still a challenge, being a motivation for the ongoing research in recent years. In [36], the authors built an LSTM-based sequence-to sequence (seq2 seq) learning model that could capture the load profiles of appliances. The LSTM network mapped a sequence of past-24-hours data to a fixed-size vector, then the appliance type was detected, and the input sequence was regenerated in a reverse form using another LSTM network. The output produced a sequence of energy consumption with resolution for the next hour. Different from this work, in [32], the authors performed a principal component analysis (PCA) feature selection, whose output is then passed to different ML models. The best results were shown by recurrent deep neural network (R-DNN). The selected features are based on historical consumption as well as environmental measurements such as temperature.