**8. Conclusions and Future Work**

that forecast environments with a considerable weather and load diversity should adopt a multi-region model for prediction of load instead of grouping the regions into a single ANN model. It can be observed that peak periods were the most difficult for the models to pre‐

dict, and the forecast results have low accuracies.

270 Decision Support Systems

**Figure 10.** STLF Model Correlation Performance in Next Day Predictions.

**Figure 11.** STLF Model Correlation Performance in Next Hour Predictions.

Load forecasting continues to grow in importance within the electric utility industry. To date, no known study has been published which examines load forecasting within the province of Saskatchewan and/or within the control area examined. The increased importance of energy and environmental concerns, coupled with enhanced regulatory presence, has renewed inter‐ est in developing an accurate and easy-to-use load forecasting system within the control area.

The general objective of this research is to conduct load forecasting for a large geographic area which has considerable weather and load diversity. The specific research objective is to develop data-driven hourly prediction models for multi-region short term load forecasting (STLF) for twelve conforming load centres within the control area in the province of Sas‐ katchewan, Canada. Since the load centres experience considerable diversity in terms of both weather and load, a multi-region based approach is needed and the ANN modelling approach was adopted for developing the models.

Due to their simplicity, ease of analysis, and long adoption history, many load forecasting systems currently used are based on a similar day methodology. However, the research re‐ sults show that the multi-region ANN model improved prediction performance over the ag‐ gregate-based short term load forecasting ANN model and the similar day aggregate model in forecasting short term aggregate loads in next hour forecasts as well as next day forecasts. All models examined were weather-driven forecasting systems. The performance of the models was evaluated using the dataset from the 2011 year. Based on the measurements of Correlation, MAE, RMSE, RAE, and RRSE, it can be concluded that the ANN-based models provide superior prediction performance over existing similar-day forecasting systems. The developed models are able to reduce STLF inaccuracies and may be applicable for model‐ ling other system concerns, such as system reliability.

Operational staff of grid control centres often adopt similar day models due to their simplic‐ ity and intuitive development, while paying less attention to the impacts of weather changes to electricity demand. This chapter has demonstrated the superior performance of the ANNbased models over the similar day models. This finding suggests that artificial-intelligencebased methods can potentially be used for enhancing performance of load forecasting in the operational environment. Future efforts in developing artificial intelligence-based forecast‐ ing systems can include efforts towards building more intuitive user interfaces, so as to pro‐ mote greater user-adoption.

We believe that merging the ANN models with other methods such as fuzzy logic, support vector regression, and time series considerations can provide enhanced consistency for mod‐ elling reduced load interval datasets. Further analysis of heat wave theory and other weath‐ er trend electricity demand drivers is necessary for these methods to become applicable for conducting both short and medium term load forecasts. The results and methods of this work will be compared against other artificial intelligence models and statistical methods to identify further areas of improvement. Future work in this field is required to decrease fore‐ cast time intervals in order to provide a real-time operating model for intelligent automated unit-commitment algorithms, which operate at 15 minute intervals. Further efforts in weath‐ er trend analysis, such as heat wave theory will be investigated in order to quantitatively describe other weather-load trends.

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