Definition
Load forecasting estimates electricity demand for a coming period (an hour, a day, a week, or a month) from historical use, weather, the calendar, the economy, and related factors. The methods are machine learning, deep learning, statistical models, and other artificial intelligence algorithms. It underpins smart-grid dispatch, demand-side response, peak shaving and valley filling, and demand-charge optimization, and it informs generation plans, grid operation, and management on the customer side.
Types and Methods of Load Forecasting
By horizon, forecasts are very short term (minutes), short term (hours and days), medium term (weeks and months), and long term (years). Common methods are classical statistics (ARIMA, exponential smoothing), machine learning (random forest, XGBoost), and deep learning (LSTM, Transformer). Inputs can include historical load, temperature, humidity, holidays, and whether a day is a workday. The data comes from smart meters, power-quality monitors, and similar devices.
Applications in the Smart Grid and Demand-Side Management
Load forecasting supports generation plans and reserve capacity on the smart grid, and the dispatch of distributed photovoltaics and energy storage systems. On the customer side, together with demand-side response and peak shaving and valley filling, it can flag a peak before it arrives and set a load plan that cuts the demand charge. A cloud platform and a data processing engine collect smart-meter data, train a model, and run it so forecasts and alarms can be produced without a manual step. SimtoLink can provide data collection and API integration for this work.
Implementation Guidance
A working forecast needs enough history (a year is a practical minimum), data that is complete and trustworthy, feature design and a chosen model, and ongoing checks. Selection should cover the interface to the smart meters and power-quality monitoring already installed, accuracy and how soon a result is available, and whether the forecast can drive demand-side response and peak shaving and valley filling. Together with power-quality monitoring, the same data can refine how electricity is used.
Forecast Horizons and Sources of Distortion
Load forecasting estimates active demand that has not happened yet, not energy already frozen in a meter. Very-short-term forecasts serve dispatch and storage actions from minutes to hours, short-term forecasts serve the next day's plan and demand control, medium-term forecasts serve weekly or monthly maintenance and power purchase, and long-term forecasts serve capacity. The four questions cannot share one error threshold: a minute-level model fears a sudden startup, while a yearly model fears a change in industrial structure. Judging annual capacity by the average error of a daily forecast does not say whether a transformer should be upsized, however small that number is.
A usable series is continuous, the metering point keeps a stable definition, and the calendar is trustworthy. Weekdays, weekends, and public holidays must be separable, and a factory must also mark maintenance and night shifts. Temperature features should come from observations in the same climate zone as the load point. Temperature at a distant airport does not represent a workshop in the mountains. Results should carry the generation time and model version, so a dispatcher can tell whether the curve was batched overnight or recalculated with new data. If an energy microgrid still has diesel units or shiftable production lines, those controllable quantities are known plan inputs. They are not forecast error.
Distortion mostly comes from patched data. Filling a communication outage with zero teaches the model that the whole plant suddenly lost power. Filling it with the average of neighboring points erases a real shutdown. If a meter is replaced but the old meter ID is kept, two curves with different ranges are joined into one, and the peak is systematically raised or lowered. Recording only photovoltaic export, and not total on-site consumption, forecasts purchased energy rather than load. Using that at noon to schedule storage discharges when it should not. If a shifted workday is labeled as a normal week, the trough of a long holiday is treated as an ordinary weekend.
Before go-live, backtest a day that has already passed but was still unknown at the time: freeze the inputs that were available then, and see what the model produces, rather than feeding it temperature measured only later. Review forecast error and peak-shaving action separately. A change in the price assumption does not prove the forecast was right. At a factory with no maintenance marks, the model learns each year's shutdown as a random drop in load, and on the next maintenance day it still reports a power peak that is not there.
Evaluation should split the error, not keep only a full-day average. Being off by ten points in the peak hour and by ten points in the trough hour has a completely different effect on demand charges. Report separately how far off the hour of maximum demand was, and whether that hour's forecast was issued a day ahead or corrected by rolling updates the same day. If a rolling correction uses load that has already occurred, it is no longer a forecast of the future and cannot be compared with the day-ahead plan for accuracy.
A newly commissioned line has no sample in the history, so the model extrapolates old startup habits. The line's plan should be an external input, rather than waiting several months and then explaining why the peak is always low. Weather features also need a check for missing values: if a whole stretch of temperature is filled with zero, the model thinks an extreme cold spell occurred. After a main transformer is replaced, power factor is adjusted, or lighting is moved to another circuit, the load range represented by the metering point has changed. The old curve and the new curve cannot be joined end to end.
Whoever publishes a forecast should be able to answer three questions: which model version was used, when the inputs were cut off, and which points were dropped because data were missing. Without those three answers, a dispatcher cannot decide whether to trust the curve. A forecast serves planning. It does not replace protection settings. However low the reported load is, overcurrent protection cannot be taken out of service on that basis.