The article describes the NN-K-SVD method based on the use of sparse coding and the singular value decomposition to specific values. An example of using the method is the compression of load profiles. The experiment of compression of 125022 power load profiles has been carried out with the use of registered profiles in households and small offices. Two matrices: patterns (atoms) and scaling factors are the result of the discussed algorithm. Features of the created matrices, which can be used in the creation of fast power demand forecasting systems, have been characterized
Short-term load forecasting is a key task for planning and stability of the current and future distr...
The main objective of this paper is to develop an efficient data compression model for online power ...
At present, the continuous increase of household electricity demand is strategic and crucial in elec...
The article describes the NN-K-SVD method based on the use of sparse coding and the singular value d...
In recent years, the electric grid has experienced increasing deployment, use, and integration of sm...
This article aims to estimate the load profiling of electricity that provides information on the ele...
The historical information of loadings on substation helps in evaluation of size of photovoltaic (PV...
Demand side management has a vital role in supporting the demand response in smart grid infrastructu...
This paper exposes a method to classify the electric consumption profiles of different types of cons...
Electricity load demand is the fundamental building block for all utilities planning. In recent year...
There is growing interest in discerning behaviors of electricity users in both the residential and c...
Non-Intrusive Load Monitoring (NILM) for residential applications aims to dis-aggregate the total el...
The exchange of data between energy stakeholders will play an important role in future smart energy ...
Time-series data is increasingly collected in many domains. One example is the smart electricity inf...
The present-day advances in technologies provide the opportunities to pave a road from conventional ...
Short-term load forecasting is a key task for planning and stability of the current and future distr...
The main objective of this paper is to develop an efficient data compression model for online power ...
At present, the continuous increase of household electricity demand is strategic and crucial in elec...
The article describes the NN-K-SVD method based on the use of sparse coding and the singular value d...
In recent years, the electric grid has experienced increasing deployment, use, and integration of sm...
This article aims to estimate the load profiling of electricity that provides information on the ele...
The historical information of loadings on substation helps in evaluation of size of photovoltaic (PV...
Demand side management has a vital role in supporting the demand response in smart grid infrastructu...
This paper exposes a method to classify the electric consumption profiles of different types of cons...
Electricity load demand is the fundamental building block for all utilities planning. In recent year...
There is growing interest in discerning behaviors of electricity users in both the residential and c...
Non-Intrusive Load Monitoring (NILM) for residential applications aims to dis-aggregate the total el...
The exchange of data between energy stakeholders will play an important role in future smart energy ...
Time-series data is increasingly collected in many domains. One example is the smart electricity inf...
The present-day advances in technologies provide the opportunities to pave a road from conventional ...
Short-term load forecasting is a key task for planning and stability of the current and future distr...
The main objective of this paper is to develop an efficient data compression model for online power ...
At present, the continuous increase of household electricity demand is strategic and crucial in elec...