Challenges in load profile monitoring: Case study

2015 ◽  
Author(s):  
Mutaz Khairalla ◽  
A.M. Gaouda ◽  
M. Abdel-Hafez ◽  
Khaled Shuaib ◽  
Mahmoud Alahmad
Keyword(s):  
Profile Monitoring ◽  
Load Profile

scholarly journals Smart Meter Data Analysis of a Building Cluster for Heating Load Profile Quantification and Peak Load Shifting

Energies ◽  
2020 ◽  
Vol 13 (17) ◽  
pp. 4343
Author(s):  
Yunbo Yang ◽  
Rongling Li ◽  
Tao Huang
Keyword(s):  
Data Analysis ◽  
Peak Load ◽  
Smart Meter ◽  
Load Shifting ◽  
Load Profile ◽  
Transition Season ◽  
Morning Peak ◽  
Heating Load ◽  
Heating Loads

In recent years, many buildings have been fitted with smart meters, from which high-frequency energy data is available. However, extracting useful information efficiently has been imposed as a problem in utilizing these data. In this study, we analyzed district heating smart meter data from 61 buildings in Copenhagen, Denmark, focused on the peak load quantification in a building cluster and a case study on load shifting. The energy consumption data were clustered into three subsets concerning seasonal variation (winter, transition season, and summer), using the agglomerative hierarchical algorithm. The representative load profile obtained from clustering analysis were categorized by their profile features on the peak. The investigation of peak load shifting potentials was then conducted by quantifying peak load concerning their load profile types, which were indicated by the absolute peak power, the peak duration, and the sharpness of the peak. A numerical model was developed for a representative building, to determine peak shaving potentials. The model was calibrated and validated using the time-series measurements of two heating seasons. The heating load profiles of the buildings were classified into five types. The buildings with the hat shape peak type were in the majority during the winter and had the highest load shifting potential in the winter and transition season. The hat shape type’s peak load accounted for 10.7% of the total heating loads in winter, and the morning peak type accounted for 12.6% of total heating loads in the transition season. The case study simulation showed that the morning peak load was reduced by about 70%, by modulating the supply water temperature setpoints based on weather compensation curves. The methods and procedures used in this study can be applied in other cases, for the data analysis of a large number of buildings and the investigation of peak loads.

scholarly journals Self-Consumption and Self-Sufficiency in Photovoltaic Systems: Effect of Grid Limitation and Storage Installation

Energies ◽  
2021 ◽  
Vol 14 (6) ◽  
pp. 1591
Author(s):  
Alessandro Ciocia ◽  
Angela Amato ◽  
Paolo Di Leo ◽  
Stefania Fichera ◽  
Gabriele Malgaroli ◽  
...  
Keyword(s):  
Cost Effectiveness ◽  
Parametric Analysis ◽  
The Self ◽  
Electric Load ◽  
Load Profile ◽  
Pv Systems ◽  
Energy Flows ◽  
Self Sufficiency ◽  
And Storage

This paper presents a methodology to maximize the self-sufficiency or cost-effectiveness of grid-connected prosumers by optimizing the sizes of photovoltaic (PV) systems and electrochemical batteries. In the optimal sizing procedure, a limitation on the maximum injection in the grid can affect the energy flows, the economic effectiveness of the investments, and thus the sizing results. After the explanation of the procedure, a case study is presented, and a parametric analysis of the effect of possible injection limits is shown. The procedure is applied to size plants for an Italian domestic prosumer, whose electric load profile was measured for a year. A software program developed using the proposed methodology is also briefly presented. It is used for both research and educational purposes, both in laboratory classes and in remote lessons.

scholarly journals Development of Load Profile Monitoring System Based on Cloud Computing in Automotive

2015 ◽  
Vol 43 (4) ◽  
pp. 573-588
Author(s):  
Hwee Cho ◽  
Ki-Tae Kim ◽  
Yun-Hee Jang ◽  
Seung-Hwan Kim ◽  
Jun-Su Kim ◽  
...  
Keyword(s):  
Cloud Computing ◽  
Monitoring System ◽  
Profile Monitoring ◽  
Load Profile

scholarly journals Influence of Data Sampling Frequency on Household Consumption Load Profile Features: A Case Study in Spain

Sensors ◽  
2020 ◽  
Vol 20 (21) ◽  
pp. 6034
Author(s):  
J. C. Hernandez ◽  
F. Sanchez-Sutil ◽  
A. Cano-Ortega ◽  
C. R. Baier
Keyword(s):  
Empirical Distribution ◽  
Full Range ◽  
Household Consumption ◽  
Sampling Frequency ◽  
Coarse Grained ◽  
Data Sampling ◽  
Load Profile ◽  
High Resolution Data ◽  
Consumption Profile

Smart meter (SM) deployment in the residential context provides a vast amount of data of high granularity at the individual household level. In this context, the choice of temporal resolution for describing household load profile features has a crucial impact on the results of any action or assessment. This study presents a methodology that makes two new contributions. Firstly, it proposes periodograms along with autocorrelation and partial autocorrelation analyses and an empirical distribution-based statistical analysis, which are able to describe household consumption profile features with greater accuracy. Secondly, it proposes a framework for data collection in households at a high sampling frequency. This methodology is able to analyze the influence of data granularity on the description of household consumption profile features. Its effectiveness was confirmed in a case study of four households in Spain. The results indicate that high-resolution data should be used to consider the full range of consumption load fluctuations. Nonetheless, the accuracy of these features was found to largely depend on the load profile analyzed. Indeed, in some households, accurate descriptions were obtained with coarse-grained data. In any case, an intermediate data-resolution of 5 s showed feature characterization closer to those of 0.5 s.

Impact of Load Profile on Dynamic Interactions Between Energy Markets: A Case Study of Power Exchange and Demand Response Exchange

2019 ◽  
Vol 15 (11) ◽  
pp. 5855-5866 ◽  
Author(s):  
Srikanth Reddy Konda ◽  
Ameena Saad Al-Sumaiti ◽  
Lokesh Kumar Panwar ◽  
Bijaya Ketan Panigrahi ◽  
Rajesh Kumar
Keyword(s):  
Demand Response ◽  
Energy Markets ◽  
Dynamic Interactions ◽  
Load Profile ◽  
Power Exchange

scholarly journals Optimal Design of an On-Grid MicroGrid Considering Long-Term Load Demand Forecasting: A Case Study

2021 ◽  
Author(s):  
Bing Han ◽  
Mingxuan Li ◽  
Jingjing Song ◽  
Junjie Li ◽  
Jamal Faraji
Keyword(s):  
Demand Forecasting ◽  
Load Prediction ◽  
Load Profile ◽  
Demand Prediction ◽  
Demand Growth ◽  
Load Demand ◽  
Cost Of Energy ◽  
Artificial Neural Network Ann

In this article, an optimal on-grid MicroGrid (MG) is designed considering long-term load demand prediction. Multilayer Perceptron (MLP) Artificial Neural Network (ANN) has been used for time-series load prediction. Yearly demand growth has also been considered in the optimization process based on the forecasted load profile. Two case studies have been performed with the forecasted and historical load profiles, respectively. It has been shown that by applying the forecasted load profile, realistic results of net present cost (NPC), cost of energy (COE) and MG configuration would be achieved. Moreover, it has been demonstrated that utilizing battery storage systems (BSSs) are not economic in the proposed system. The introduced MG also produces lower emission compared to the system with the historical load profile.

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