Electricity forecasting on the individual household level enhanced based on activity patterns

Gajowniczek, Krzysztof; Ząbkowski, Tomasz

doi:10.1371/journal.pone.0174098

Cited by 84 publications

(76 citation statements)

References 36 publications

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“…This implies that life-style, due to the economic affluence of households, may play a vital role in the demand of electricity. The findings of this paper coincide with some previous studies, as many researchers also indicate that the amount of household appliances may play an important role in affecting electricity consumption in the residential sector [43][44][45].…”

Section: Discussionsupporting

confidence: 91%

The Factors Affecting Electricity Consumption and the Consumption Characteristics in the Residential Sector—A Case Example of Taiwan

Chen

2017

Sustainability

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This article attempts to examine the factors affecting residential electricity consumption through two approaches, a socioeconomic perspective and a direct use perspective. The results of this study find that gross domestic product (GDP), employment rates, residential space, and the implementation of energy labeling schemes provide significant impacts on residential electricity consumption. However, the impacts of electricity price and the energy efficiency standards do not receive significant support. The analysis of the direct use approach finds that air conditioners consume the largest portion of electricity, amounting to 1470 kWh for each household and accounting for 26.81% except for lighting. Refrigerators and rice cookers follow, consuming 815.83 kWh (14.88%) and 343.85 kWh (6.27%) of electricity. The correlation analysis reveals that GDP keeps a high relationship with the installation of electrical appliances and eventually leads to an increase in residential electricity consumption. By integrating the results obtained from these two approaches, this paper also discusses some improvement strategies. The results, derived from the case example of Taiwan's residential electricity consumption, may provide valuable information for policy-making involving green labeling, energy standards, and electricity prices.

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Section: Discussionsupporting

confidence: 91%

The Factors Affecting Electricity Consumption and the Consumption Characteristics in the Residential Sector—A Case Example of Taiwan

Chen

2017

Sustainability

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“…According to [13] load forecasting on the individual household level is a challenging task due to the extreme system volatility as a result of dynamic processes composed of many individual components. Home loads can be influenced by a number of factors, such as: operational characteristics of devices, behaviours of the users, economic factors, time of the day, day of the week, weather conditions, holidays etc.…”

Section: Accuracy a Datasetmentioning

confidence: 99%

“…According to [13], the forecasting performance at the individual level shows much higher errors (20% to 100% and even higher), and depends on dwelling lifestyle and regularity of appliance usage as described above. In Figure 1a we depict the calculated MAPE for each endpoint that was considered.…”

Section: Accuracy a Datasetmentioning

confidence: 99%

Methodology for managing cost-effective demand response campaigns based on demand elasticity profiles

Anastopoulou

Kalogiros

Stamoulis

2017

2017 Sustainable Internet and ICT for Sustainability (SustainIT)

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Abstract-We develop and evaluate a methodology for running manual implicit and explicit Demand Response campaigns in order to improve the sustainability of smart grids. Initially, we introduce a flexibility profiling engine that relies on the correlation among end-user's consumption, prices and environmental conditions. Then, we investigate two mechanisms so that an Aggregator, acting as an intermediate between the Distribution System Operator or Retailer and the consumers, achieves the desired demand flexibility. More precisely we introduce: (i) a price-based mechanism that determines the new price that consumers will be paying during the DR campaign, and (ii) a reward-based mechanism that determines which set of consumers should participate in the campaign, along with the load flexibility to be asked from each one and the reward offered. The proposed methodology is evaluated using a publicly-available dataset. It is shown that CES model achieves high accuracy and that the details obtained using the proposed mechanisms are more favorable than the equivalent used. 1

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“…Moreover, with the development of economy and living standards, energy consumption in the household sector has contributed to an increasing proportion of aggregate energy consumption (e.g., about 12% in China, 27% in European, 36% in American). Therefore, it is necessary and meaningful to study the modelling method of the household load profile with high prediction accuracy [4].…”

Section: Introductionmentioning

confidence: 99%

A Bottom-Up Model for Household Load Profile Based on the Consumption Behavior of Residents

2018

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Abstract:The forecasting of the load profile of the domestic sector is an area of increased concern for the power grid as it appears in many applications, such as grid operations, demand side management, energy trading, and so forth. Accordingly, a bottom-up forecasting framework is presented in this paper based upon bottom level data about the electricity consumption of household appliances. In the proposed framework, a load profile for group households is obtained with a similar day extraction module, household behavior analysis module, and household behavior prediction module. Concretely, similar day extraction module is the core of the prediction and is employed to extract similar historical days by considering the external environmental and household internal influence factors on energy consumption. The household behavior analysis module is used to analyse and formulate the consumption behavior probability of appliances according to the statistical characteristics of appliances' switch state in historical similar days. Based on the former two modules, household behavior prediction module is responsible for the load profile of group households. Finally, a case study based on the measured data in a practical residential community is performed to illustrate the feasibility and effectiveness of the proposed bottom-up household load forecasting approach.

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Electricity forecasting on the individual household level enhanced based on activity patterns

Cited by 84 publications

References 36 publications

The Factors Affecting Electricity Consumption and the Consumption Characteristics in the Residential Sector—A Case Example of Taiwan

The Factors Affecting Electricity Consumption and the Consumption Characteristics in the Residential Sector—A Case Example of Taiwan

Methodology for managing cost-effective demand response campaigns based on demand elasticity profiles

A Bottom-Up Model for Household Load Profile Based on the Consumption Behavior of Residents

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