Error Analysis of Customer Baseline Load (CBL) Calculation Methods for Residential Customers

Mohajeryami, Saeed; Doostan, Milad; Asadinejad, Ailin; Schwarz, Peter

doi:10.1109/tia.2016.2613985

Cited by 79 publications

(44 citation statements)

References 18 publications

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“…mean and standard deviation (Std) in forecasting day (D #no represents the number of day) for sky cover, dew point, relative humidity, and temperature. In addition, the result of Bias defined by (19) is also shown in the table.…”

Section: Simulation Resultsmentioning

confidence: 99%

On the performance of forecasting models in the presence of input uncertainty

Sangrody

Sarailoo

Zhou

et al. 2017

2017 North American Power Symposium (NAPS)

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Abstract-Nowadays, w ith the unprecedented penetration of renewable distributed energy resources (DERs), the necessity of an efficient energy forecasting model is more demanding than before. Generally, forecasting models are trained using observed weather data while the trained models are applied for energy forecasting using forecasted weather data. In this study, the performance of several commonly used forecasting methods in the presence of weather predictors with uncertainty is assessed and compared. Accordingly, both observed and forecasted weather data are collected, then the influential predictors for solar PV generation forecasting model are selected using several measures. Using observed and forecasted weather data, an analysis on the uncertainty of weather variables is represented by MAE and bootstrapping. The energy forecasting model is trained using observed weather data, and finally, the performance of several commonly used forecasting methods in solar energy forecasting is simulated and compared for a real case study.

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Section: Simulation Resultsmentioning

confidence: 99%

On the performance of forecasting models in the presence of input uncertainty

Sangrody

Sarailoo

Zhou

et al. 2017

2017 North American Power Symposium (NAPS)

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“…In the literature, a number of baseline estimation methods have been proposed and can be generally categorized into similar-day approaches, morning-adjustment approaches, regression approaches, and CONTROL group approaches [6], [7]. First, similar-day methods aim to select the most similar days prior to the event day based on the weather conditions and then averages them to construct the final estimated baseline demand (e.g., LowXofY [8], HighXofY [9]), which may result in tremendous estimation error due to neglecting information of the event day (demand response event). To this end, morning-adjustment methods are proposed by using the actual measured pre-event load data to adjust the estimated baseline demand for further enhancing the estimation performance [10].…”

Section: Introductionmentioning

confidence: 99%

Preheating Quantification for Smart Hybrid Heat Pumps Considering Uncertainty

Sun

Štrbac

Djapić

et al. 2019

IEEE Trans. Ind. Inf.

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The deployment of smart hybrid heat pumps (SHHPs) can introduce considerable benefits to electricity systems via smart switching between electricity and gas while minimizing the total heating cost for each individual customer. In particular, the fully-optimized control technology can provide flexible heat that redistributes the heat demand across time for improving the utilization of low-carbon generation and enhancing the overall energy efficiency of the heating system. To this end, accurate quantification of preheating is of great importance to characterize the flexible heat. This paper proposes a novel data-driven preheating quantification method to estimate the capability of heat pump demand shifting and isolate the effect of interventions. Varieties of fine-grained data from a real-world trial are exploited to estimate the baseline heat demand using Bayesian deep learning while jointly considering epistemic and aleatoric uncertainties. A comprehensive range of case studies are carried out to demonstrate the superior performance of the proposed quantification method and then, the estimated demand shift is used as an input into the whole-system model to investigate the system implications and quantify the range of benefits of rolling-out the SHHPs developed by PassivSystems to the future GB electricity systems.

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“…(1) The bias of CBL cannot be avoided, and it will result in welfare losses to customers or LSEs [41]. In addition, DR event frequency will be much higher in large-scale DR to integrate a higher proportion of RES, which causes obstacles to establishing accurate CBL, as it is calculated by historical load data in the absence of DR events.…”

Section: Introductionmentioning

confidence: 99%

A Novel Distributed Large-Scale Demand Response Scheme in High Proportion Renewable Energy Sources Integration Power Systems

Fan

Jia

et al. 2018

Applied Sciences

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Large-scale demand response (DR) is a useful regulatory method used in high proportion renewable energy sources (RES) integration power systems. Current incentive-based DR schemes are unsuitable for large-scale DR due to their centralized formulation. This paper proposes a distributed scheme to support large-scale implementation of DR. To measure DR performance, this paper proposes the customer directrix load (CDL), which is a desired load profile, to replace the customer baseline load (CBL). The uniqueness of CDL makes it more suitable for distributed schemes, while numerous CBLs have to be calculated in a centralized manner to ensure fairness. To allocate DR tasks and rebates, this paper proposes a distributed approach, where the load serving entity (LSE) only needs to publish a total rebate and corresponding CDL. As for each customer, s/he needs to claim an ideal rebate ratio that ranges from 0 to 1, which indicates the proportion of rebate s/he wants to get from LSE. The rebate value for each customer also determines his or her DR task. Then, the process of customer claims for the ideal rebate ratio is modeled as a non-cooperative game, and the Nash equilibrium is proved to exist. The Gossip algorithm is improved in this paper to be suitable for socially connected networks, and the entire game process is distributed. Finally, a large-scale DR system is formulated. The simulation results show that the proposed DR can promote the consumption of RES. Additionally, this scheme is suitable for large-scale customer systems, and the distributed game process is effective.

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Error Analysis of Customer Baseline Load (CBL) Calculation Methods for Residential Customers

Cited by 79 publications

References 18 publications

On the performance of forecasting models in the presence of input uncertainty

On the performance of forecasting models in the presence of input uncertainty

Preheating Quantification for Smart Hybrid Heat Pumps Considering Uncertainty

A Novel Distributed Large-Scale Demand Response Scheme in High Proportion Renewable Energy Sources Integration Power Systems

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