The addition of solar panels to forecasting energy demand and peak energy demand presents an entirely new challenge to a facility. By having to account for the varying energy generation from the solar panels on any given day based on the weather it becomes increasingly difficult to accurately predict energy demand. With renewable energy sources becoming more prevalent, new methods to track peak energy demand are needed to account for the energy provided by renewable sources. We know from previous research that Artificial Neural Networks (ANN) and Auto Regressive Integrated Moving Average (ARIMA) models are both capable of accurately forecasting building demand and peak electric load days without the presence of solar panels. The goal of this research was to take three different approaches for both the ANN model and the ARIMA model to find the most accurate method for forecasting monthly energy demand and peak load days while considering the varying daily solar energy production. The first approach used was to forecast net demand outright based on relevant historical training data including weather information that would help the models learn how this information affected the overall net demand. The second approach was to forecast the building demand specifically based on the same relevant historical data and then use a random decision tree forest to predict the cluster of day that each day of the month would be in terms of solar production (high, medium with early peak, medium with late peak, low). After the type of day was predicted we would subtract the average solar energy production of the predicted cluster to receive our forecasted net demand for that day. The third approach was similar to the second, but instead of subtracting the average of the cluster we subtracted multiple randomly generated days from that cluster to provide multiple overlapping forecasts. This was specifically used to try and better predict peak load days by testing the hypothesis that if 80% or higher predicted a peak day it would in fact be a peak day. The ANN model outperformed the ARIMA for each approach. Forecasting multiple days was the best of the three approaches. The multiple day ANN forecast had the highest balanced accuracy and sensitivity, the net demand ANN approach was the 2nd most accurate approach and the average solar ANN forecast was the 3rd best approach in terms of balanced accuracy and sensitivity. Based on the outcomes of this study, consumers and institutions such as RIT will be better able to predict peak usage days and use preventative measures to save money by reducing their energy intake on those predicted days. Another benefit will be that energy distribution companies will be able to accurately predict the amount of energy customers with personal solar panels will need in addition to the solar energy they are using. This will allow a greater level of reliability from the providers. Being able to accurately forecast energy demand with the presence of solar energy is going to be critical with the ever-increasing usage of renewable energy.

Library of Congress Subject Headings

Solar energy; Energy consumption--Forecasting

Publication Date


Document Type


Student Type


Degree Name

Industrial and Systems Engineering (MS)

Department, Program, or Center

Industrial and Systems Engineering (KGCOE)


Katie McConky

Advisor/Committee Member

Nasibeh Azadeh Fard


RIT – Main Campus

Plan Codes