The Problem of Electricity Consumption in Data Mining

Published: 2021-09-14 23:20:09
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Category: Computer Science, Experience, Physics

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The economic growth and the potential increase in population is on the higher side since the beginning of the 20th century. There are statistics that among the overall energy consumption, the building energy consumption is seen increase over 50% in recent time. For instance, the energy consumption of building will increase approximately 35% in 2 years from now. Hence, it is high time there should be some measures that can help us predict the peak usage time and also prevent excess use of electricity. These kinds of prediction can help customers when buying new machines, decision makers to decide on the selling price per unit electricity and it will also aid in the investment plans and the like.
The demand for electricity and consumption rate has been rapidly increasing hence the electricity transmitters must be able to meet the demand for the growing population and large-scale industrialization in the developing countries. This project is about predicting the peak of electricity consumption using the estimated population of France except Corsica. The consumption peak is a challenge to electricity providers since they must over dimension the grid to support the high consumption load. Prediction of electricity that would be consumed by the people is done in the field of data mining using the machine learning algorithms. The peak prediction would help the utility providers to balance the supply and demand in country level. Based on the prediction, policies can be formulated for the profitability of electricity transmission firm.Data mining process is used to extract meaningful information to identify and understand the trends in the data. Data mining is a one part of the whole procedure in the process that uses perceptive algorithms to find the repetitions. It is one of the influential concepts that can be used to understand the relations and predict the happenings based on the common features and the attributes from the previously occurred events in the dataset. There is enormous amount of data that is being gathered every time unit which we need to exploit and work towards the betterment of the social and economic growth by right planning for the energy consumption. A better knowledge of the electricity end-uses in residences can allow people to make more possibilities to lower their electricity bills, help them make use of the non- peak hours, and allow governments to better forecast energy demand and address climate changes.
Data mining can be applied to a scope of domains and the ones that are social are a good contestant for the research. The energy aspect in the country is very important as it decides the economic growth of the country and the welfare of the people in the country. The energy usage has been developing on a rapid scale as all the nations invest money on large scale to increase the smartness of the energy distribution to provide a legitimate and timely response.
There are several factors that affect the consumption of the electricity. When we consider an office, which is one of the highest consuming area, it is a mandate that we analyze the consumption of electricity according to the time and environment. This will be useful in estimating the load of the power consumption and deliver electricity accordingly. Even though a lot of studies and researches are already in place for estimating the power consumption, there is also fact that much more efforts are required in rightly quantifying the peak usage load and make useful evaluations out of it.
There are a number of methods that were proposed to predict the usage of electricity. In order to work and predict the outcome of the consumption, we need a series of consumption data for building the data mining and machine learning models. In general, the methods of prediction can be seen as two broad umbrellas. Any model could be either a statistical model or a machine leaning based prediction model.
The energy usage prediction models are in general important for yet another reason. The prediction of usage of energy on an accurate scale can help with right management of the power grids. If the outcome of the prediction is precise, then the situation of the power grid failures can be largely avoided. In the recent days, the electricity is also used in different useful ways. For example, people use electricity for electric cars and other renewable power utilities. This adds to the total consumption of the electricity and hence the power grids and distribution agencies must accordingly be resourced to satisfy the needs of the consumer.
The models need to be smart that it can help in controlling and managing the power consumption to help both the consumer and the electric energy supplier. It is obvious that 60% of the population will reside in cities where the cost of electric energy is more and it is useful for the industry to have a machine learning model for predicting and analyzing the peak usage details so they can make corrective actions if needed. The idea is lack of knowledge about the information on the consumption of the power should not be a show stopper for the city to become smart. It is important that we use the electric energy efficiently, plan for the future use, use energy to the fullest possible, avoid interruption in power supply. All these attributes will become possible once we have a model that can statistically analyze the power consumption and predict the peak usage time for quality services. Hence, it is equally important to understand that the model will help in efficient distribution of electricity with no loss. The research is involved in field of data mining, machine learning, patter recognition and data visualization for predicting the real time electric data. The data mining algorithms are used to reduce the raw data and learn the pattern.
A lot of analytical researches has been conducted and revealed that the forecasting model was required for efficient distribution of the electricity. With this model, the electric energy vendors can also provide analytics to the consumer so that they can plan for the energy usage. Additionally, this data will be useful for the electricity vendor to monetize more during the peak time as resources are fully used. This kind of research will help in understanding the gap in the demand and the supply of electricity. Over and above of the demand forecast, it is useful when we are able to predict for the power generation and a model for analyzing the power saving options. We propose a model that can predict the peak energy usage details. We understand the data set cover various features like the consumption units, the consumption behavior, generation source, etc.
Strategizing the energy distribution and usage is very necessary for the developing countries. As these countries have huge impacts and growing economy, the rate at which the electricity is being consumed is on a higher scale and fluctuating, it is hard to predict the usage with higher accuracy. It is important for the policy makers to use these predictions effectively in making organization policies while developing strategies. It is vital for the results to be accurate and hence we will compare the results with the real time data so that we can measure the accuracy without any bias. The demand of electricity and its trend curve is very complex and hence it is essential for the forecasting model to be strong that it suits all the patterns of consumption.
There had been raising concern for effective energy management as the growth of economy is more of recent. It is important to have a model that can predict the usage and help us with the statistics. Data mining and machine learning algorithms can help us build this model. There are several methods using different machine learning and statistic models. The analysis on the time series data of electricity consumption works on investigation, visualization, identification of patterns and matches. Prediction is data sensitive and time sensitive. A prediction hit will reduce the impact of the abnormal consumption or prevent it from happening. Nevertheless, a prediction miss will add to the wastage of the resources, efforts and time of the energy supplier in working around a wrongly predicted peak consumption. Hence, an extreme care and caution is required in choosing the dataset, preprocessing the data set and cleaning it. Another area of concentration has to be with choosing the right algorithm to analyze the data and predict the events of a peak consumption given the test attributes. Since, there exists an enormous amount of data, any flaw in the model will make it unusable in the long run. The mining of the data is one of the proven techniques in the field of predictive analysis. This kind of research is also known to identify the contributing factors to the increasing consumption rate as we visualize the trend from the data. It is also important to build predictive models according to the type of the consumption data that will help in more accurate results from the model. This process involves choosing the right attributes for prediction of peak consumption which means to eliminate the attributes that are of less use for the prediction. The inference from this kind of model can be relied on and the preventive and corrective actions can be focused.

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