The increase in the number of residents has an impact on the increasing number of electricity customers each year, resulting in an increase in electricity consumption. In order to achieve the adjustment of generation and demand for electrical energy needs electricity system planning is needed, one of which is forecasting the consumption of electrical energy in the future. This aims to realize optimization in the process of supplying electrical energy. In this study, the application of the fuzzy logic method is used to process the rules of the input variables and produce forecasting of long-term annual electricity consumption in East Kalimantan Province until 2028. In addition, the Exponential Smoothing method is also used as a comparison method to determine the accuracy of forecasting calculation models. Electrical energy forecasting has characteristics that are usually influenced by factors such as population, number of customers, GRDP and so on. Input data used for learning fuzzy logic in this forecasting are the actual data for 2010-2018 and the projected input data for 2019-2028 are used to obtain forecasting results. Then the forecasting data for 2019-2028 is compared with the PLN RUPTL projection data. From the results of forecasting, the best level of accuracy with the smallest error value is obtained using the fuzzy logic method that is with a MAPE value of 3.80%. This research is expected to be a consideration for forecasting electricity consumption models for the planned transfer of Indonesia’s new capital in the area of East Kalimantan Province.

Long-term forecasting of electricity in East Kalimantan Province using the fuzzy logic approach

Affected by weather, geography and other factors, PV output has fluctuations and intermittentness. It is not suitable to supply power to the load independently. It needs to be used together with the energy storage device. At the same time, the power load has a great influence on the energy storage configuration. Therefore, accurate load forecasting has Helps improve the stability and reliability of the power system, saves electricity costs, and facilitates dynamic optimization of optical storage. In this regard, this paper proposes a dynamic optimization strategy for optical storage based on long-term and short-term memory networks. Firstly, the long-short-term memory neural network method is used to predict the power load. Secondly, according to the power difference adjustment and the photovoltaic output result, the daily-time load is divided into four sections, and the different sections respectively correspond to specific control strategies. Finally, according to each, the interval control strategy optimizes the energy storage and discharge power in real time. The method stores excess photovoltaic energy and releases it in the form of electric energy when the user's power grid needs it. This can effectively suppress the power fluctuation of photovoltaic power generation, improve the utilization rate of photovoltaics, and perform peak clipping for the user grid Valley regulation.

https://www.dpi-proceedings.com/index.php/dtcse/article/download/27305/26715

Research on Dynamic Optimization Operation of Optical Storage Based on LSTM Load Forecasting

This paper presents selected similar days of short-term power load forecasting model based on fuzzy clustering, the method first meteorological factors subdivided into temperature, barometric pressure, wind speed, rain, etc., and then type the week, date, type of day together constitute similar factors, fuzzy coefficient feature mapping table through fuzzy rules, not only to achieve quantitative impact factors and facilitate real-time to add a new rule. On this basis, the use of fuzzy clustering method for classification, based on the level of clustering similar day is selected to reduce the number of samples to accelerate the selected speed. The model takes into account the full impact of weather and other factors on load forecasting, further weakening the load of randomness. Simulation results show that the method has higher prediction accuracy.

Cheng Lu

Papers

Short Term Load Forecasting Based on Fuzzy Clustering