Machine Learning-Based Electricity Load Forecast for the Agriculture Sector

1. Introduction

Power forecasting is required for consumers, utilities, distributors and generators as it plays a vital role in electricity procurement and planning (Kaytez et al., 2015). Consumers of different sectors demand energy in various purpose and need. Thus, load forecasting is a challenging task to regulate the demand and supply of electricity in different sectors. Energy demand forecasting (Alvarez et al., 2010) is essential in every sector but in case of agriculture sector it becomes challenging to forecast due to various factors such as climate, rainfall, and land type. Existing work, forecasting energy demand in the agriculture sector relies on factors from national averages. However, energy demand varies by season, crops, land area, agricultural machinery and technologies, pump sets, and groundwater (Kumar, 2005). In India, after the sixth five-year plan period (1980-1985), agricultural output growth increased due to improved seed quality, usage of fertilizers, and irrigation. Modern farming requires energy as an input in all stages of agricultural production such as farming, water management, irrigation, agricultural machinery and harvesting. Out of these stages the power consumption for motor pump irrigation is high so it is necessary to forecast the power consumption for this sector (Moulik et al., 1990).

According to the 2011 census, 61.5% of India's population is rural and is dependent on agriculture. The contribution of the agriculture sector to the Indian GDP is 14.4% (as per the 2018-19 Economy Survey). Electricity consumption is increasing every year and the agriculture sector also has high energy consumption every year for the purpose of irrigation, agricultural machinery and cold storage.

The total power consumption in the year 2018-19 was 1158 TWh and growth rate of 2018-19 as compared to 2017-18 was 3.11% and the Compound Annual Growth Rate (CAGR) was 6.58% from 2009-10 to 2018-19. The agriculture sector shared 8% of total consumption in 1980 and 17.49% of the total consumption in 2018-19. In 2018-19, 207 TWh was consumed by the sector, and the growth rate of 2018-19 over 2017-18 was 4.29% and the CAGR rate 2009-10 to 2018-19 was 7.43% (Energy Statistics, 2020). The electricity supplied is subsidized or free of charge and most areas are without meters (Prayas (Energy Group), 2018). Farmers need incentives to grow suitable crops, but they are wrongly blamed for high electricity consumption and face poor-quality electricity due to lack of meters. Accurate forecasting in the agriculture sector benefits the farmers, utilities, and the government. The low level of metering and unmetered connection challenges in the agriculture sector to find the true level of power consumption and to predict electricity, because when utilities deliver electricity, it becomes difficult to predict how much electricity a consumer consumes and how much electricity is lost due to technical and commercial reasons. Farmers having the issue of poor-quality electricity supply and overestimation of the electricity consumption occurs in the agriculture sector that leading to the underestimation of the poor-quality supply. To accurately estimate the power consumption, there is a need to install a meter in the fields. In the presented work, power consumption (metered and flat) is considered for estimating future consumption.