The power utility industry has become highly volatile with a deregulated market on the horizon and with enormous profit and loss swings in the energy trading. Abstract. The electricity demand is significantly dependent on the weather information. utilised to capture the relationship between the per capita demand to. A method is described for statistically analysing the relationship between weather and electricity demand for applications to practical load estimating by.
After introducing the Smart World, a global framework for the collaboration of these smart systems, this paper presents the relationship found at experimental level between a range of relevant weather variables and electric power demand patterns, presenting a case study using an agent-based system, and emphasizing the need to consider this relationship in certain Smart World and specifically Smart Grid and microgrid applications.
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Introduction We are living a time of change marked by governments' attempts to promote energy efficiency and renewable energy sources against an increasing power demand.
In the last years, conventional power supply models are being replaced with new ones capable of meeting these two challenges at the same time.
One strategy is to understand the existing relationship between energy demand and climatic variables, so as to better forecast and adapt the former to the later in real time. In order to do drive this adaptation, the Smart Grids count with huge sensor networks to exhaustively measure all kinds of power generation, power demand and climate variables in real time.New Zealand Wintery Weather and Electricity Price Impact.
Social and political structures are increasingly concerned about climatic change and environmental awareness. In [ 1 ], Pili-Sihvola et al. In [ 2 ], Messaoud and Chen assess how climate change affects the height growth of different tree species in the region of British Columbia, showing that variations in weather variables affect the height growth of a range of plants.
Parkpoom and Harrison [ 3 ] in their work presented how climate change will affect electric power demand in the long term in Thailand by using regression models to obtain the correlations between power demand patterns and temperature.
Among the many factors influencing energy demand, a number of studies have demonstrated that weather variables influence energy consumption patterns. Considine [ 5 ] assessed the impact of climate change on energy demand and carbon dioxide emissions. Hyndman and Fan [ 7 ] used a semi-parametric additive model to estimate the relationship between electric power demand, temperature, working days and demographic and social factors, to predict peak loads in the long term.
As the relationship between electric power demand and temperature is not linear, another study empirically investigates this non-linearity, using both parametric and non-parametric methods, as shown by Henley and Peirson [ 8 ]. The analysis performed by Terasvirta and Anderson in [ 9 ] proposes a set of smooth-transition autoregressive models for the evolution from a cold threshold temperature to a warm threshold temperature.
As geographical factors are also essential in weather forecasting, Psiloglou et al. All these studies assess how environmental and weather conditions affect the behavior of living beings.
Electric power is indispensable and strategic to national economies. Consequently, electric power supply companies try to adapt power supply to the demand. The following studies present electric load forecasting models based on Artificial Neural Network ANNwhich includes weather variables. A study conducted in Korea presented a forecasting model where energy demand was predicted for specific daily hours basing on a combination of load data and temperature, and using a Multi-Layer Perceptron MLPaccording to Kim et al.
The conclusions drawn in all these studies suggest that environmental indicators and weather variables should be monitored, as they might be used as input in a set of specific applications—as it is the case of electric load forecasting, and they can represent a threat to the future of plants, animals and even humans. Furthermore, modern ICT and sensors should be exploited in electric power demand forecasting to facilitate the operation and control of forecasting systems.
However, while the literature presents several studies of the relationship between weather variables and electric load, they are usually focused on large areas and regions and are not directly portable to smaller environments like Smart Grids. One of the advantages of these smart systems is that they are capable of providing precise answers to local problems thanks to distributed intelligence, and as such, the objective of this work is to particularize the correlation analysis to the Smart Grid scale using an adequate data set, and present a Smart Grid design to take advantage of this data in real time.
In newly industrialized countries such as China and India increased urbanization and industrial growth create significant drivers for electricity demand. In India, electricity demand is expected to more than quadruple, and in China it is supposed to more than double by In total, non OECD electricity demand will surge by percent by On the other hand, the OECD countries will experience demand rise about 25 percent bywith the United States accounting for the largest increase in demand and representing the 50 percent of the growth in OECD electricity use.
In addition, the fuel mix significantly changes by region. Non OECD countries will experience growth across all fuel types except oil throughwith coal capturing the largest share, followed by natural gas.
In China, India and in Africa, especially in the South Mediterranean Rim, countries seek to diversify their electricity supply through the development of renewable energies that are becoming a larger part of the fuel mix although their contribution remains relatively small at less than 10 percent. In OECD countries, a transition from coal to natural gas, driven by the emergence of greenhouse gas policies and the shale gas revolution, along with an additional share of renewable and nuclear generation will become more significant after The challenge in the next 30 years is that non OECD countries will lead growth in electricity demand, where the need of electricity is linked mainly to growing urbanization, improvement of standards of living, as well as to climate change.
In short, in these countries due to climate variability electricity demand will grow with the aim to desalinate the water and to switch on the air conditioning. Scenario A1b assumes that there will be global rapid economic growth in the future and that energy will come from a balanced range of fossil fuel and non-fossil fuel sources.
The project aim was not that of forecasting actual electricity demand in a future economy, but to make an estimation of the effect of temperature change on electricity demand on the present economy. Results indicated that an increase in temperature has an impact on electricity consumption four times the size of the equivalent decrease in temperature and that there is likely to be an increase in electricity demand in the South of Europe.
In the Northern European countries it is estimated that there will be a fall in consumption. For example, Latvia would reduce its consumption by For central Europeans, the increases in summer temperatures and reductions in winter temperatures come fairly close to leveling out over the year. Compared to the potential impacts of changes in income, demography and technology, these effects are small.
However, the estimation does not consider future changes in supply or the more detailed regional and seasonal effects on supply and transfer of electricity, which may have an impact on price and therefore consumption. For example, there may be greater demand in hotter and colder months or in certain geographical areas.
Lastly, the study does not include other effects of climate change that might indirectly influence electricity demand, such as changes in wealth and energy efficiency, or new technologies such as electric cars.
Variations in average annual temperature due to climate change for the A2 scenario are expected to lead to an increase in electricity consumption per capita, equivalent to an annual increase of 1. Under the B2 scenariothe average annual increase in electricity consumption per capita over the baseline value is expected to be 1.
These economic impact estimates are equivalent to a loss of 0.
In Thailand, the electric utility company EGAT serves mainly agricultural, residential and commercial customers. Among these customers many are increasing their standards of living.
This factor coupled with the growing temperatures make the use of air conditioning an important share of the use of electricity. Air conditioning systems are heavily used in the summer, but also in some other periods of the year.