Temperature and seasonality influences on Spanish electricity load

Pardo, Á.; Meneu, Vicente; Valor, E.

doi:10.1016/s0140-9883(01)00082-2

Cited by 295 publications

(142 citation statements)

References 6 publications

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“…But in winter the opposite of the above will happen, during winter decrease in per degree temperature will results in increase of electric load. For this negative correlation Pardo et al proposed a transfer function intervention model to predict the electricity demand in the hot and cold days [7]. This is because during summer increase in temperature affect the people's feeling of comfort and consumers will use electricity for cooling purpose (more AC and fans will be used), whereas in winter electricity is used for heating purposes.…”

Section: A Temperature Temperature Is the Measure Of Average Kineticmentioning

confidence: 99%

Factor Affecting Short Term Load Forecasting

Fahad¹,

Arbab²

2014

JOCET

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Abstract-The basic objective of short term load forecasting is to predict the near future load for example next hour load prediction or next day load prediction etc. The total system load is the load seen at the generating end of the power system, which includes the sum of all types of loads connected to the system plus the losses. To design efficient and accurate forecasting model one must have good understanding of the characteristics of the system. There are various factors which influence the behavior of the consumer load and also impact the total losses in transmission lines. These factors can be categorized as Time factor, weather, economy and random disturbances. In this research paper these factors and their impact on consumption of electric power and their significance in short term load forecasting is evaluated.Index Terms-ANN, factors of STLF, load forecasting, load prediction. I. TIME FACTORTime is the most important factor in short term load forecasting because its impact on consumer load is highest. From observing load curve of several different grid stations it is found that the load curve has "time of the day" property, also it has "day of week", "week of month" and "month of season" property. This means that load curve is periodic in nature. Fig. 1(a)- Fig. 1(c) shows the load curve of 3 Fridays of the April 2012.As can be seen in Fig. 1(a)- Fig. 1(c) that although the average load is different because of the difference of temperature during each day but the pattern of the load is nearly same. In each curve there arises three peaks First peak at 8 o clock, 2 nd peak at 12:30 pm, And third at 8 pm and in curves first peak is lowest 2 nd a bit taller and third the highest. This shows periodicity in terms of day of the week. Manuscript received August 17, 2013; revised December 16, 2013. This work was carried out as a final year project for the award of MS degree in electrical engineering.Muhammad Usman Fahad is with the University of Engineering and Technology, Peshawar,.Naeem Arbab is with the Electrical Engineering Department of University of Engineering and Technology, Peshawar, Pakistan (e-mail: mnarbab01@yahoo.com). From the observation of daily load curve of any grid station it can be seen that load variation follows certain rules with the "time point" of the day.In Fig. 2 hourly load curves is shown for 24 hours of a day in winter season. The interval length is one hour, so there are 24 intervals in the figure. It can be seen in figure that the load is low and stable from 0 am to 6 am (0am means 12 o clock at night), the load start rising at 7 o clock till 9 am and then it is flattened till 12 am and after that it descends till 5 pm (17:00 in figure) and after 5 pm it start rising till 8 pm (19:00), after 8 pm load gradually decrease again until the end of the day.It can be seen in Fig. 2 that maximum demand occurs at 8pm, and minimum load demand occurs after mid night. So if we closely observe this load curve it can be seen that load demand reflects the consumer's daily life style. At mid...

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Section: A Temperature Temperature Is the Measure Of Average Kineticmentioning

confidence: 99%

Factor Affecting Short Term Load Forecasting

Fahad¹,

Arbab²

2014

JOCET

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“…A fixed periodic/cyclical pattern, that often is assumed caused by the lifestyle of the households, can be modelled using dummy variables (Granger et al, 1979;Pardo et al, 2002) or trigonometric terms (Al-Zayer and Al-Ibrahim, 1996;Granger et al, 1979), or by the use of splines (Hendricks et al, 1979;Harvey and Koopman, 1993). We modelled the cyclical patterns with dummy variables; one set with dummy variables for the 24 hours of the working days and one set for the 24 hours of the non-working days.…”

Section: Methods and Modelmentioning

confidence: 99%

“…A rich literature on the temperature's effect on electricity consumption suggests that the impact of a temperature change has non-linear, as well as delayed effects; see, for example, Peirson (1997, 1998), Granger et al (1979), Harvey and Koopman (1993), Ramanathan et al (1997) and Pardo et al (2002). Following Granger et al (1979) we allowed for the current temperature by one term and its possible non-linear influence by a squared term.…”

Section: Methods and Modelmentioning

confidence: 99%

Direct load control of residential water heaters

Ericson

2009

Energy Policy

136

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Abstract:In Norway there is a growing concern that electricity production and transmission may not meet the demand in peak-load situations. It is therefore important to evaluate the potential of different demand side measures that may contribute to reduce peak load. This paper analyses data from an experiment where residential water heaters were automatically disconnected during peak periods of the day. A model of hourly electricity consumption is used to evaluate the effects on the load of the disconnections. The results indicate an average consumption reduction per household of approximately 0.5 kWh/h during disconnection, and an additional average increase in consumption the following hour, due to the payback effect, of approximately 0.2 kWh/h.

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“…Linear regression model proves to be very useful for predicting short-term peak loads [10,29] and annual loads [43]. The later research uses support vector regression and various functional linear regression models for the actual prediction.…”

Section: Regression Modelsmentioning

confidence: 99%

A Pattern Recognition Approach for Peak Prediction of Electrical Consumption

Goodwin

Yazidi

2014

Progress in Pattern Recognition, Image Analysis, Computer Vision, and Applications

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Abstract.Demand peaks in electrical power consumptions pose serious challenges for energy companies as these are typically unforeseen and require the net to support abnormally high consumption levels. Such peaks can be regulated in smart energy grids with the introduction of relatively simple techniques such as load balancing and smart pricing strategies. This is, however, difficult in practice because it requires prediction of peaks prior to their actual occurrence.While most studies formulate the problem as an estimation problem, we take a radically different approach and formulate it as a classical pattern recognition problem. Further, the paper applies classification methods to solve the problem and applies these with real-life data from a Norwegian smart grid pilot project. Some of the key findings are that the algorithms can accurately detect 80% of energy consumption peaks up to one week ahead of time. Furthermore, we introduce a novel Learning Automata based approaches for selecting the optimal prediction model from a pool of models in an online fashion.

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Temperature and seasonality influences on Spanish electricity load

Cited by 295 publications

References 6 publications

Factor Affecting Short Term Load Forecasting

Factor Affecting Short Term Load Forecasting

Direct load control of residential water heaters

A Pattern Recognition Approach for Peak Prediction of Electrical Consumption

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