Short-Term Load Forcasting for Smart Power Systems Using Swarm Intelligence Algorithm

Abstract

In order to succeed in our everyday life, efficient performance of the power system is of utmost importance, and hence, all the sub-sectors of the power system should be analyzed for the purpose of achieving efficiency and accuracy. It must also be remembered that load forecasting assists a lot to improve the power system. Moreover, it contributes substantially to formulate logical approaches for emerging short-term load forecasting (STLF) for all days including the distinct days and make them follow a uniform standard. Of all the techniques which have been applied so far, honey bee-optimized Euclidean norm, based on fuzzy inference system, is used for identifying the problems, and in addition, support vector classifier is utilized to prepare the STLF models. Parameters — temperature, humidity, monsoons, wind, cloud density, dew point, season, hour of the day, day of the week, distinct day, and holiday — have been taken into account for the current study. A well-prepared database can be used for regression which will be of immense help to forecast the load using artificial intelligence. For every day of a month, the MAPE is computed (using the forecasted and actual hourly values) in order to observe the accuracy of STLF. The planned method has been very successful for the load forecast of all days for all seasons. The forecast has been done using the technique for a real time data of one year (test forecast year) with a historical dataset collected for a period of two years, and the results obtained for all seasons have been found to be satisfactory. STLF has helped to find better values due to its pace, and become healthier than other methods already in practice. With the advent of smart grid, the data will be accessible at more granular level as smart meters have capability to provide consumer load, usage data on-line and this facility will be of great help to utility operators and planners for operations on-line. How to use the data available from smart meters for better STLF is a challenging task and it would draw much attention for future research.

The Application of Artificial Intelligence Technology in Ideological and Political Education

“…People's thoughts and concepts, emotional orientations, hobbies, and daily behaviors in the real world may be mapped to some digital existence, and people's thoughts and behaviors thus become measurable, recordable, predictable, and clusteranalyzable.‖ Intelligent data becomes the second body of human beings [10].‖ "Once the relevant data was fully grasped, it is possible to completely predict and grasp individuals' behavioral tendencies and dispositions using digital drawing [11]." The data portrait of people's thoughts and behaviors opens new possibilities for people to understand themselves and profoundly changes them [12]. Their ecology has been realized, and the revolutionary leap for classification technology of ideological and political education objects has been established, thus establishing the internal relationship between the two [13].…”

A weighted plain Bayesian algorithm-based method for automatic scheduling of short-term load resources in power grids

“…In a related study, Hiba et al aim to solve the load forecasting problem by combining and testing different multimodal deep learning methods and architectures to improve forecasting performance in order to process and correlate information from multiple modalities [3].Prakash et al mention that with the advent of smart grids, data will be available at a more granular level due to the ability of smart meters to provide customer load and usage data online accessible, a facility that will be of great help to utility operators and online operational planners [4]. How to use the data provided by smart meters to improve short-term load forecasting is a challenging task that will attract a great deal of attention for future research.…”