Abstract
The load of transformers shows higher volatility and uncertainty than do the system-level and substation-level loads. This paper proposes a two-stage short-term load forecasting (STLF) model for power transformers. 1) Three state-of-the-art technologies are applied to predict the aggregated substation-level load by taking the historical load, weather, and calendar data as inputs. In this stage, no specific STLF model needs to be developed, which allows the forecasters to select the most accurate prediction results for transformer-level load forecasting. 2) The load distribution factor (LDF) is defined as the ratio of the transformer load to the substation load. The relationship between LDF and substation load is captured by nonlinear regression functions under different substation operating conditions, and the load of each parallel transformer is predicted using these nonlinear regression functions. Each nonlinear function can be accurately established even if the historical load data are scarce under some irregular operating conditions. Three application examples show the effectiveness and rationality of the proposed method. The third example demonstrates that STLF of transformers is necessary because it provides important information for optimizing substation operating schemes and equipment maintenance plans.
Highlights
Short-term load forecasting (STLF) commonly consists of hourly prediction of the load from one day to one week ahead
The relationship between load distribution factor (LDF) and substation load is captured by nonlinear regression functions under different substation operating conditions, and the load of each parallel transformer is predicted using these nonlinear regression functions
This paper presents a two-stage STLF model for power transformers
Summary
Short-term load forecasting (STLF) commonly consists of hourly prediction of the load from one day to one week ahead. The stepwise extrapolation forecasting model can be established using different ANNs. For instance, the extreme learning machine (ELM) is applied to realize STLF by inputting previous daily load segments, temperature, and day type [9]. In contrast to the stepwise extrapolation forecasting model, the aggregation of similar load segments has no training process in many cases, which can be utilized when historical load data are insufficient or nonconsecutive. This method becomes complicated and time-consuming in large datasets because load selection and aggregation should be implemented for each segment to be predicted.
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