Handling Data Heterogeneity in Electricity Load Disaggregation via Optimized Complete Ensemble Empirical Mode Decomposition and Wavelet Packet Transform

Abstract

Global warming is a leading world issue driving the common social objective of reducing carbon emissions. People have witnessed the melting of ice and abrupt changes in climate. Reducing electricity usage is one possible method of slowing these changes. In recent decades, there have been massive worldwide rollouts of smart meters that automatically capture the total electricity usage of houses and buildings. Electricity load disaggregation (ELD) helps to break down total electricity usage into that of individual appliances. Studies have implemented ELD models based on various artificial intelligence techniques using a single ELD dataset. In this paper, a powerline noise transformation approach based on optimized complete ensemble empirical model decomposition and wavelet packet transform (OCEEMD–WPT) is proposed to merge the ELD datasets. The practical implications are that the method increases the size of training datasets and provides mutual benefits when utilizing datasets collected from other sources (especially from different countries). To reveal the effectiveness of the proposed method, it was compared with CEEMD–WPT (fixed controlled coefficients), standalone CEEMD, standalone WPT, and other existing works. The results show that the proposed approach improves the signal-to-noise ratio (SNR) significantly.

A Comprehensive Review on Smart Grids: Challenges and Opportunities

“…Neighborhood Area Networks (NAN) [22][23][24][25][26][27][28][29][30][31][32][33][34][35][36][37] Software Defined Networks (SDN) [20,[38][39][40][41][42][43] Interdependent networks (IN) [44][45][46][47][48][50][51][52][53] Field Area Networks (FAN) [21,49,[54][55][56][57][58][59][60][61][62][63]69] Wireless Sensor Networks (WSN) [64][65][66][67][68][69]...…”

“…Note that 17% can find both in NANs and WSNs, respectively, pointing out a great opportunity to develop smart grids with softwaredefined topologies. Internet of Things [19,23,24,27,28,30,31,36,37,58,64,66,73,74,79,105,107] Machine learning [20,29,32,51,55,56,60,62,69,78,80,81,85,98,102,106] Data mining [47,107] Machine learning and neural training [21,54,69,81,96] Short term memory network [41,61] Power Line Communication Technology [53,63,90] Power electronics [35,50,99,101,<...>…”

“…Table 3 shows that most of the Encryption issues do not clearly define the algorithms they use, although there are some articles that use Multi-dimensional Data aggregation and even Cognitive Risk Control. Multi-dimensional Data aggregation [42,[55][56][57]60,62,64] Cognitive Risk Control [37,82] Not Defined [19,20,[22][23][24]26,28,29,31,35,36,38,39,43,[45][46][47][48]50,[52][53][54]58,61,63,73,[76][77][78][79]81,[98][99][100][101][102][103][104][105]…”

“…Although there are some schemes that contemplate the Direct Current for very specific applications in the Industry. Direct Current [28,48,55,88,99,107,108] Alternate Current [19,[21][22][23][24][25][26][27][29][30][31][35][36][37][38][40][41][42][43][45][46][47]50,[52][53][54][56][57][58][59][60][61][62][63]69,73,76,78,79,98,[108][109][110] In terms of the Type of current transmitted by the Smart Grid, Figure ...…”

“…There are other articles that include topics affecting any human society. For instance, the work proposed by Chui et al [56] mentions the concern about the warming of our planet, generating very important changes in almost all parts of the world. Due to the above, that work is focused on reducing the energy consumption in homes by making it more intelligent, i.e., consumption is adapted and there are no unnecessary energy costs.…”