Although the market valuation and adoption of IoT in various sectors is in an uptrend, the actual deployment is lagging when compared to the industrial forecasted data. The main reason behind this drawback is the lifespan of IoT devices due to their limited battery capacities. A solution to the problem is to deploy energy harvesting units (e.g., solar to replenish the batteries). However, due to the time varying availability of both irradiance and temperature and their effect on the power output, it is essential to predict both variables. To this end, we propose in this paper a prediction system that does not consume a lot of energy and that can be deployed on low computational nodes. This model consists of a Seasonal Auto Regressive Integrated Moving Average (SARIMA) with a Kalman filtering (KF) component. We build this model using an actual dataset for Ottawa, Ontario, Canada. We then demonstrate its effectiveness by presenting the results for randomly selected days in the Winter Season. In this context, we show that the SARIMA-KF outperforms the SARIMA in all scenarios with an average error reduction of 59.3%.