Modelling the Effect of Temperature on Power Generation at a Nigerian Agricultural Institute

Abstract

The energy sector is considered one of the most sensitive sectors to climate change. Climate change has a considerable impact on environmental weather parameters such as temperature, humidity, radiation from the sun, precipitation, sunshine hours, wind direction, etc. These meteorological considerations have an impact on the electricity consumption rate. As a result, knowing the influence of weather conditions on energy demand and consumption is critical for adapting, planning, and forecasting the impact of changing climate on an organization's energy needs. Several factors influencing electricity consumption can be classified as economic, seasonal, and meteorological factors. This research aims to look at the influence of climate change on energy supply in a typical agricultural institute and utilize Artificial Neural Network (ANN) and Multivariate Linear Regression (MLR) models to predict the impact of changes in temperature on electricity generated. The approach used in this study includes: Creating a database of weather variables and energy demand or consumption parameters; analyzing and correlating electrical energy demand to weather variables, developing models - Multivariate Linear Regression (MLR) and Artificial Neural Networks (ANN) to forecast the impact of change in the weather variables on the electrical energy. “Average temperature” was seen to have the most influence on electrical energy with the highest correlation (r = 0.92 for 2015 and r = 0.86 for 2011 - 2018), while “Wind speed” had the least influence with the lowest correlation (r = 0.033 for 2011 - 2018). The ANN model was the best of the two models considered in this study. The mean squared error was reduced by 39% and 42% on test data and train data, respectively, indicating that ANNs outperformed the MLR model. Other measures, such as Mean Absolute Error (MAE) and Mean Absolute Percentage Error (MAPE), showed that the ANN performed substantially better than the MLR. The results suggest that ANN models perform relatively well since the algorithm learns independently and develops a reasonably accurate representation of the dataset.