Abbreviation:
DNN	Deep neural network
LSTM	Long short-term memory network
CNN	Convolutionary neural network
FCM	Fuzzy C-means algorithm
STLF	Short-term load forecasting
Load consumption forecasting:
N	Number of forecasting features
C	Number of time-invariant features
$(N-C)$	Number of time-varying features
$x\left(t\right)$	Load consumption at time t
$e\left(t\right)$	Noise resident at time t
$x(t-p)$	Past load consumption with p time sample lag
$\widehat{x}(t+m)$	Predicted future load consumption with m time samples ahead
$y_i\left(t\right)$	$i^{th}$ forecasting feature
$\overline{y}\left(t\right)$	Forecasting feature vector containing all $y_i\left(t\right)$
$\overline{F}(t-p,t)$	Past information set containing $\overline{y}\left(t\right)$ and $x\left(t\right)$ from time $t-p$ to t
${\overline{y}}_I$	Forecasting feature vector containing time-invariant features
${\overline{y}}_I$	Time-invariant vector containing time-invariant features
${\overline{y}}_V\left(t\right)$	Time-varying vector containing time-varying features
${\overline{Y}}_V$	Time-varying set containing time-varying vectors