A stochastic modelling and simulation approach to heating and cooling electricity consumption in the residential sector

García, Emilio José; Moreno-Munoz, A.; Santiago, I.; Flores-Arias, J.M.; Bellido-Outeiriño, F.J.; Moreno-Garcia, Isabel M.

doi:10.1016/j.energy.2017.12.082

Cited by 27 publications

(11 citation statements)

References 46 publications

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“…The service sector integrates a heterogeneous group of activities, basically related to the office, commerce, hotels and restaurants, health, and education sectors, and this sector has its greatest presence between 7:00 and 19:00 h ( REE, 2019 ). In the paper of ( Palacios-Garcia et al, 2018 ) the activity curves of different industrial and service sectors can be seen, in days of continuous and split schedule, and a great part of the activity developed in the afternoon has been suspended during the alarm state, giving rise to the consumption decrease observed at these hours. And in the last hours of the day, the night peak has also changed its shape, it has a shorter duration, of only about 1 h, when previously it had a duration of about three hours, and a lower demand value.…”

Section: Resultsmentioning

confidence: 99%

“…But it is also noteworthy that the energy injected into the grid as surplus has been reduced by 32.89% with the 2020 demand profile. Although these data cannot be generalized since they correspond to a particular house, with a demand profile conditioned by working hours ( Endesa, 2019 ) and the analysis corresponds to only two months of the year, in which consumption for air conditioning is also low ( Palacios-Garcia et al, 2018 ), certain conclusions can be extracted. Firstly, there is still a high demand for electricity at night peaks, even if one stays in the house all day, partly due to the inertia of the habits residents had and which results in a significant part of the electricity consumption in the house taking place at these hours when there is no production of PV energy.…”

Section: Resultsmentioning

confidence: 99%

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Electricity demand during pandemic times: The case of the COVID-19 in Spain

et al. 2021

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Electricity demand and its typical load pattern are usually affected by many endogenous and exogenous factors to which the generation system must accordingly respond through utility operators. Lockdown measures to prevent the spread of COVID-19 imposed by many countries have led to sudden changes in socioeconomic habits which have had direct effects on the electricity systems. Therefore, a detailed analysis of how confinement measures have modified the electricity consumption in Spain, one of the countries most affected by this pandemic, has been performed in this work. Its electricity consumption has decreased by 13.49% from March 14 to April 30, compared to the average value of five previous years. Daily power demand profiles, especially morning and evening peaks, have been modified at homes, hospitals, and in the total power demand. These changes generate a greater uncertainty for the System Operator when making demand forecasts, but production deviations have increased by only 0.1%, thanks to the presence of a diversified generation mix, which has been modified during this period, increasing the proportion of renewable sources and decreasing CO 2 emissions.

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Section: Resultsmentioning

confidence: 99%

Section: Resultsmentioning

confidence: 99%

Electricity demand during pandemic times: The case of the COVID-19 in Spain

et al. 2021

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“…Besides, HDDs are obtained by subtracting base temperature from outdoor temperature. Different methods for calculations of CDDs and HDDs have been investigated by Palacios-Garcia et al 68 ACs energy demand in buildings is observed in different time spans for energy management by CDDs and HDDs indices. These indices reveal the severity of summer and winter conditions for any location in terms of the outdoor temperature.…”

Section: Estimation Of Acs Demand Based On Cdds and Hddsmentioning

confidence: 99%

Optimal BRA based electric demand prediction strategy considering instance‐based learning of the forecast factors

Waseem

Liu

Zhang

et al. 2021

Int Trans Electr Energ Syst

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With the grid's evolution, the end-users demand becomes more vital for demand side management (DSM). Accurate load forecasting (LF) is critical for power system planning and using advanced demand response (DR) strategies.To design efficient and precise LF, information about various factors that influence end-users demand is required. In this paper, the impact of different factors on electrical demand and capacity of climatic factors existence and their variation is discussed and analysed. The Pearson correlation coefficient (PCC) is utilized to express the degree of electric demand correlation with metrological and calendar factors. Then, the optimal-Bayesian regularization algorithm (BRA) based on ANN for LF is presented. The effect of the number of neurons in hidden layers on output is observed to select the most appropriate option. Additionally, heating degree days (HDDs) and cooling degree days (CDDs) indices are investigated to consider the impact of air conditioners' (ACs) loads in different seasons. Case studies on data from Dallas, Texas, USA, are used to

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“…Physical models are mathematical representations of heat and mass transfer phenomena between buildings, people, and the environment. For instance, Palacios-Garcia et al [68] developed a high-resolution model for calculating the electricity demand of heating and cooling appliances, considering variables such as the number of residents, location, type of day (weekday or weekend) and date. In [69], a model for simulating lighting power consumption profiles in Spain was developed, considering the number of household residents and differentiating between weekdays and weekends.…”

Section: Electric and Thermal Energy Demandmentioning

confidence: 99%

A Technical Review of Modeling Techniques for Urban Solar Mobility: Solar to Buildings, Vehicles, and Storage (S2BVS)

et al. 2020

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The deployment of solar photovoltaics (PV) and electric vehicles (EVs) is continuously increasing during urban energy transition. With the increasing deployment of energy storage, the development of the energy sharing concept and the associated advanced controls, the conventional solar mobility model (i.e., solar-to-vehicles (S2V), using solar energy in a different location) and context are becoming less compatible and limited for future scenarios. For instance, energy sharing within a building cluster enables buildings to share surplus PV power generation with other buildings of insufficient PV power generation, thereby improving the overall PV power utilization and reducing the grid power dependence. However, such energy sharing techniques are not considered in the conventional solar mobility models, which limits the potential for performance improvements. Therefore, this study conducts a systematic review of solar mobility-related studies as well as the newly developed energy concepts and techniques. Based on the review, this study extends the conventional solar mobility scope from S2V to solar-to-buildings, vehicles and storage (S2BVS). A detailed modeling of each sub-system in the S2BVS model and related advanced controls are presented, and the research gaps that need future investigation for promoting solar mobility are identified. The aim is to provide an up-to-date review of the existing studies related to solar mobility to decision makers, so as to help enhance solar power utilization, reduce buildings’ and EVs’ dependence and impacts on the power grid, as well as carbon emissions.

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A stochastic modelling and simulation approach to heating and cooling electricity consumption in the residential sector

Cited by 27 publications

References 46 publications

Electricity demand during pandemic times: The case of the COVID-19 in Spain

Electricity demand during pandemic times: The case of the COVID-19 in Spain

Optimal BRA based electric demand prediction strategy considering instance‐based learning of the forecast factors

A Technical Review of Modeling Techniques for Urban Solar Mobility: Solar to Buildings, Vehicles, and Storage (S2BVS)

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