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⛄ 内容介绍
Accurate load forecasts are critical for short term operations and long term planning for utilities. The load forecast influences a number of decisions including which generators to commit for a given period, and broadly affects the wholesale electricity market prices. Load and price forecasting algorithms typically also feature prominently in reduced-form hybrid models for electricity price, which are some of the most accurate models for simulating markets and modeling energy derivatives. The electricity price forecast is also used widely by market participants in many trading and risk management applications. The load forecast influences a number of decisions including which generators to commit for a given period, and broadly affects the wholesale electricity market prices. Load forecasting algorithms typically also feature prominently in hybrid models for electricity prices, some of the most accurate class of approaches for modeling electricity markets. The electricity price forecast is used widely by market participants in many trading and risk management applications. Traditionally, utilities and marketers have used commercial software packages for performing load forecasts. The main disadvantage of these is that they are a black box, offering no transparency into how the load forecast is calculated. They also only typically offer 80-90% of the functionality needed by a utility. In many cases it is just not possible to meet all of the requirements through an off-the-shelf product, for instance taking into account regional loads, different weather patterns and so on. MathWorks tools provide the flexibility of building a completely customized load forecasting system that meets 100% of the requirements. And because of the built-in models, high-level language and ease of connecting to data, the time taken to develop such a system is also dramatically lower than building an equivalent system in a lower level programming language, as is demonstrated in this example.
⛄ 部分代码
function data = fetchDBPriceData(startDate, endDate)
% Set preferences with setdbprefs.
s.DataReturnFormat = 'structure';
s.ErrorHandling = 'store';
s.NullNumberRead = 'NaN';
s.NullNumberWrite = 'NaN';
s.NullStringRead = 'null';
s.NullStringWrite = 'null';
s.JDBCDataSourceFile = '';
s.UseRegistryForSources = 'yes';
s.TempDirForRegistryOutput = 'C:\Temp';
s.DefaultRowPreFetch = '10000';
setdbprefs(s)
% Make connection to database. Note that the password has been omitted.
% Using ODBC driver.
conn = database('EnergyData','','password');
% Read data from database.
e = exec(conn,['SELECT ALL Date,Hour,DryBulb,DewPnt,SYSLoad,NGPrice,ElecPrice FROM NEData WHERE Date BETWEEN #' startDate '# AND #' endDate '# ']);
e = fetch(e);
close(e)
% Assign data to output variable.
data = e.Data;
data.NumDate = datenum(data.Date, 'yyyy-mm-dd') + (data.Hour-1)/24;
% Close database connection.
close(conn)
⛄ 运行结果
⛄ 参考文献
[1]黄宏运, 朱家明, 吴礼斌. 基于神经网络的短期电力负荷预测及其MATLAB实现[J]. 太原师范学院学报：自然科学版, 2016, 15(3):6.
