Relationships between meteorological variables and monthly electricity demand

Apadula, F.; Bassini, Alessandra; Elli, Alberto; Scapin, Simone

doi:10.1016/j.apenergy.2012.03.053

Cited by 174 publications

(93 citation statements)

References 19 publications

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“…This explains that, in Table 4 To quantify what portion of the overestimate of HVAC source energy by the AMYs in tables 6 and 7 is attributable to the high bias of solar radiation, there is a need to study the correlation between the key weather variables and the simulated building performance. Apadula et al [34] studied the effect of the meteorological variability on the national monthly electricity demand in Italy. A multiple linear regression model based on calendar and four weather variables, including air temperature, wind speed, relative humidity and cloud cover, is developed to study the relationships between meteorological variables and electricity demand as well as to predict the monthly electricity demand up to 1 month ahead.…”

Section: Discrepancies Of Weather Data From Different Sources and Difmentioning

confidence: 99%

A fresh look at weather impact on peak electricity demand and energy use of buildings using 30-year actual weather data

2013

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Buildings consume more than one third of the world's total primary energy. Weather plays a unique and significant role as it directly affects the thermal loads and thus energy performance of buildings. The traditional simulated energy performance using Typical Meteorological Year (TMY) weather data represents the building performance for a typical year, but not necessarily the average or typical long-term performance as buildings with different energy systems and designs respond differently to weather changes. Furthermore, the single-year TMY simulations do not provide a range of results that capture yearly variations due to changing weather, which is important for building energy management, and for performing risk assessments of energy efficiency investments. This paper employs large-scale building simulation (a total of 3162 runs) to study the weather impact on peak electricity demand and energy use with the 30-year (1980 to 2009) Actual Meteorological Year (AMY) weather data for three types of office buildings at two design efficiency levels, across all 17 ASHRAE climate zones. The simulated results using the AMY data are compared to those from the TMY3 data to determine and analyze the differences. Besides further demonstration, as done by other studies, that actual weather has a significant impact on both the peak electricity demand and energy use of buildings, the main findings from the current study include: 1) annual weather variation has a greater impact on the peak electricity demand than it does on energy use in buildings; 2) the simulated energy use using the TMY3 weather data is not necessarily representative of the average energy use over a long period, and the TMY3 results can be significantly higher or lower than those from the AMY data; 3) the weather impact is greater for buildings in colder climates than warmer climates; 4) the weather impact on the medium-sized office building was the greatest, followed by the large office and then the small office; and 5) simulated energy savings and peak demand reduction by energy conservation measures using the TMY3 weather data can be significantly underestimated or overestimated. It is crucial to run multi-decade simulations with AMY weather data to fully assess the impact of weather on the long-term performance of buildings, and to evaluate the energy savings potential of energy conservation measures for new and existing buildings from a life cycle perspective.

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Section: Discrepancies Of Weather Data From Different Sources and Difmentioning

confidence: 99%

A fresh look at weather impact on peak electricity demand and energy use of buildings using 30-year actual weather data

2013

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“…The effect of weather variables on load forecasting in mid-term horizon is extensively studied in refs. [73][74][75]. Autonomous approach has been widely accepted in MTLF modelling, where the historical load and weather data are the main load impacting variables.…”

Section: Mid-term Load Forecasting Overviewmentioning

confidence: 99%

Forecasting the load of electrical power systems in mid‐ and long‐term horizons: a review

Khuntia

Rueda

Meijden

2016

IET Generation, Transmission & Distribution

134

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Load forecasting has always been an important part in the planning and operation of electric utilities, i.e., both transmission and distribution companies. With technological advancement, change in economic condition and many other factors (to be discussed in this work), load forecasting is becoming more important. The forecast affects as well as gets affected because of the load impacting factors and actions taken in different time horizons. However, due to its stochastic and uncertainty characteristics, it has been one challenging problem for electrical utilities to accurately forecast future load demand. This paper aims at reviewing the different load forecasting techniques developed for the mid-and long-term horizons of electrical power systems. Since there has never been an explicit literature study of the various forecasting techniques for mid-and long-term horizons, this paper reviews techniques for each of the forecasting horizons, citing various methodologies developed so far supported by published literature. The study is concluded with discussion on future research directions.

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“…Studies have revealed that a large proportion of the variability in electricity demand is dependent on weather variables such as air temperature, humidity, wind speed, cloud cover and luminosity [9,10]. The sensitivity of electricity demand in the commercial and residential sectors to meteorological variables is higher than in the industrial sector [11]. Both the weather and illumination components are very dependent on the hour of the day so they will have an impact on the daily load profile.…”

Section: Introductionmentioning

confidence: 99%

“…It is determined ultimately by data availability [11]. The Energyplus dataset has many weather variables, any or all of which could be considered for inclusion in the ANN input vector.…”

Section: Choosing the Input Variables Of The Ann Predictor: Sensitivimentioning

confidence: 99%

“…In some cases, in addition to historical load data, both weather variables (temperature, relative humidity, wind velocity and cloudiness) and socio-economic factors have been used as inputs to the forecasting model [11]. Only recently, the synthetic generation of energy production or consumption using public data is starting to be recognized as a viable alternative to measurement and recording.…”

Section: Introductionmentioning

confidence: 99%

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Generation of synthetic benchmark electrical load profiles using publicly available load and weather data

Pillai¹,

Putrus²,

Pearsall³

2014

International Journal of Electrical Power & Energy Systems

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Electrical load profiles of a particular region are usually required in order to study the performance of renewable energy technologies and the impact of different operational strategies on the power grid. Load profiles are generally constructed based on measurements and load research surveys which are capital and labour-intensive.In the absence of true load profiles, synthetically generated load profiles can be a viable alternative to be used as benchmarks for research or renewable energy investment planning. In this paper, the feasibility of using publicly available load and weather data to generate synthetic load profiles is investigated. An Artificial Neural Network (ANN) based method is proposed to synthesize load profiles for a target region using its Typical Meteorological Year 2 (TMY2) weather data as the input. To achieve this, the proposed ANN models are first trained using TMY2 weather data and load profile data of neighbouring regions as the input and targeted output. The limited number of data points in the load profile dataset and the consequent averaging of TMY2 weather data to match its period resulted in limited data availability for training. This challenge was tackled by incorporating generalization using Bayesian regularization into training. The other major challenge was facilitating ANN extrapolation and this was accomplished by the incorporation of domain knowledge into the input weather data for training. The performance of the proposed technique has been evaluated by simulation studies and tested on three real datasets. Results indicate that the generated synthetic load profiles closely resemble the real ones and therefore can be used as benchmarks.

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Relationships between meteorological variables and monthly electricity demand

Cited by 174 publications

References 19 publications

A fresh look at weather impact on peak electricity demand and energy use of buildings using 30-year actual weather data

A fresh look at weather impact on peak electricity demand and energy use of buildings using 30-year actual weather data

Forecasting the load of electrical power systems in mid‐ and long‐term horizons: a review

Generation of synthetic benchmark electrical load profiles using publicly available load and weather data

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