applied sciences  
Model for Integrating the Electricity Cost Consumption and Power Demand into Aggregate Production Planning  
**[PERSON] 1,2\({}^{\text{\textregistered}}\)**, [PERSON] 1, [PERSON] 2,3,  
**[PERSON] 4,4\({}^{\text{\textregistered}}\)**, [PERSON] 5\({}^{\text{\textregistered}}\) and [PERSON] 1\({}^{\text{\textregistered}}\)**  
and environmental impact [4]. It is estimated that if consumption in developing countries continues to follow the current trend, it may surpass that of developed countries [5]. The current challenge for developing countries is to ensure more efficient and sustainable resource management to minimize climate and environmental impacts related to energy production [6]. Unfortunately, Brazil's energy consumption data indicate that the country follows the same line as other developing countries, with increasing consumption over time. Since the Brazilian energy matrix is heavily dependent on water resources, the low rainfall due to the seasons can become a bottleneck for the Brazilian society and its production chain. In the country, some seasons are systematically characterized by a low amount of rainfall, which influences water resources [5; 7]. Given this, decision-makers in Brazil must know how to manage energy consumption better, reducing dependence on thermoelectric plants since they burn fossil fuel, pollute, and have higher generated energy costs, unlike hydroelectric plants. In this scenario, it is essential that companies efficiently manage their energy consumption, minimize the impact that bad weather has on energy supply, guarantee the efficiency of their operations, and optimize their electricity costs. The efficient use of electrical energy is a way for companies to reduce operating costs and increase their economic performance [8; 9]. Several studies in the literature present predictions about energy consumption. [PERSON] et al. [10] employed Machine Learning models and Artificial Neural Networks to predict the demand and ensure the supply to customers of energy supply networks. [PERSON] et al. [8] used prediction models to predict energy consumption; the application case was implemented in energy pipelines to meet the needs and optimize the management. [PERSON] et al. [11] used regression models to predict industrial energy demand in Morocco. [PERSON] et al. [12] developed a model to manage energy demand and predicted prices in a way that allows companies to reduce energy costs. On the other hand, no works are found in the literature which differentiates demand forecasting and aggregate production planning. Aggregate production planning is a way of managing production capacity. Usually, it is carried out considering a period of 2 to 18 months to meet the fluctuation in demand [13]. The aim is to determine the optimal amount of raw material, production, workforce, and inventory levels for each planning period considering limited resource capacities [14; 15]. Given the identified gap, this study proposes and applies a model that integrates the cost of electricity consumption and power demand to aggregate production planning. The proposed model allows production planning to consider medium-term energy demand forecasts to reduce waste in procurement in a food production company in Brazil. This study can assist in the energy planning of companies from different industrial segments since ANEEL (National Electric Energy Agency) Resolution 414/2010 determines that consumers of high voltage electricity must previously contract a power demand to be used throughout the contract period [16]. This requires companies to plan production to meet, as accurately as possible, the amount of demand needed. To meet the demands of companies, this study aims to answer the research question: how can electric energy and power demand be considered for cost reduction in aggregate production planning?